Spiral: Designing an AI Ghostwriter With Taste

Most AI writing is sloth. Unless you're

spending hours babysitting prompts,

you're getting generic output. And when

you do put in that effort, you're not

writing. You're holding the AI's hand,

explaining what you want, reprompting,

guiding it towards something usable and

that sounds like you. I'm Danny, the

general manager of Spiral. I spent 7

months rebuilding it from the ground up

to solve this problem. Spiral is now an

AI writing partner with Taste. In this

conversation with AI and I host Dan

Shipper, you'll hear how I use AI tools

like Droid and Claude Code to build a

really beautiful and intentional AI

writing experience, how we taught models

to judge their own writing, and the

philosophy behind making an AI writing

partner instead of a slot machine. By

the way, this intro was co-written by

Spiral. Let's get into it.

seven, eight months. We went through a

ton of iter iterations and then you were

like, I want to make this the most

beautiful [ __ ] app I've ever made.

Like, show show us the app right now.

Show us what it looks like.

>> Okay, so here we are. This is the new

spiral. Um, and there's like so many

tiny moments that we like thought about

on this. Um, you know, James and I and

Lucas, we all really just like spend a

lot of time thinking about like what is

the experience that you want to have

when you come here. Um, and you know,

it's very subtle. This is one thing that

I just like I love it every single time.

But we have this like slight papery

feeling effect. It's noise, but in the

background it feels like it's paper. Um,

like you know, we intentionally chose

this like tight face. Um,

>> wait, I want to pause you here. Like

just looking at this, right? There's so

much here. There's obviously like what

are we writing today, Danny? There's and

and for people who are listening,

there's like a um there's a there's a

there's a box where you, you know, you

type in the the post you want to write

about. And then there's like a plus

button where you can attach more

context. There's a microphone button

where you can talk to it. There's a

styles button where you can add styles.

But then there's a whole sidebar of

different workspaces. And within each

workspace, you have different products.

There these little icons. It's like

there's one for Kora. So if you want to

write write about Kora another product

like you can go in there and and and

that's all beautifully designed and then

there's like a history and what what I

want to emphasize to your point is you

built all of this by yourself

that is crazy that like that would have

been totally impossible a year ago um or

two years ago and uh and so I think

you're I think you're making a really

important point about what you can

achieve with these tools if you use them

well and you want to make something

great.

>> Yeah, I think it really, you know, it

allows you to go from like idea to

something really quickly. And I think

you can go from something to like

something beautiful also really quickly.

And you know,

we we I worked with some really talented

people like Lucas, James, you know, we

we all jumped in here. And I think the

really cool thing was that it allowed,

you know, both Lucas and James who are

not engineers to like mess around and

clawed and be like, "Oh, can we figure

this out?" Like to your point of, you

know, being able to speak to it, we have

like a monologue integration and we have

this like dithering animation and I

think Lucas made the first one in like a

code sandbox using um Claude and then we

like iterated from there and then messed

around in Figma. like it allows also a

bunch of people to jump in and you know

who aren't necessarily engineers to like

help play around and like take it to

that final um place. Um

>> I so that makes a lot of sense you know

for people who who are not as familiar

with the every ecosystem u monologue is

another product um that we build

internally and so now spiral connects to

monologue which is super cool and that

would actually be kind of fun to talk

about but why don't you show us um how

spiral works just so we get a sense for

okay once you're like opening the

Pandora's box of it like what is

possible and then what I actually what's

what's really interesting to me right

now is I want learn more about your

workflow because I think we do a ton of

um cloud code uh content uh on every and

you are actually a droid boy and uh and

I feel like you have some some ways of

working that are maybe a little bit

different from some of the stuff that

we've done um previously and this is

really making me you know one of the

things I feel about you is just from

looking at your hair and how how

beautiful your hair is um you and and

also that leather jacket that you're

wearing. You really have this u and

you've said this before about yourself,

you have this like um measure twice, cut

cut once mentality where I you're just

like you're a craftsman. And um I think

that that has come through in this

product. It has come through in how you

um think about building a tool for

writers. And um and I and I think that

that uh to your to a point you made

earlier, it um

it's sort of at odds with what people

think you can build with AI. It's like,

oh, you're just going to make slop and

actually you can build build beautiful

things with AI and I think you're a

really good example of that. So let's

start with um using Spiral.

>> Okay, thank you for those lovely words

by the way. Um

so I actually prepared beforehand um

talking about this interview with spiral

and I thought that could be kind of meta

to go through and see how it worked. So

first of all I did it inside the spiral

workspace and the whole idea of

workspaces. It's kind of like projects

in charge of your claude which is like

it just has documents and stuff and

context beforehand. Um and so it knows

what you're talking about. And so I

literally said like hey I'm about to

jump on a podcast with Dan to talk about

you. Help me prepare some notes. And

what it actually did, oh that was a

little janky. I got to fix that. But um

one of the things that it did was that

it it asked itself, what do I know about

myself? And it kind of like thought

about it and it sort of spat it all out

and was like, hey, here are a bunch of

things that you could talk to about Dan.

Like most AI writing is slop. Spiral has

taste. Um we can talk about what makes

Spiral different. It chats first and it

interviews. It explores three different

angles. These are all things to come. Um

we can also talk about the evolution

story. So Spiral like suggested a bunch

of things to me. Um, and then I could,

you know, it then the first thing the

spiral does is it asks questions. Um, it

always wants to know more concrete

details from you. Um, and so it asks,

would you like me to expand or help you

through think through specific stories

or examples that you could share? And so

I said, actually, one of the other

things I want to talk about is like the

sheer number of iterations that we went

through testing and experimenting. And

then Spiral again look through what it

knew of the story of Spiral and kind of

came through here are these different

like arcs you know the realization the

old one isn't working the pivot and then

it started ask me questions for each one

like what was the first thing that I

tried differently when did the chat

first insight emerge um how many

iterations did I go through what did you

learn about what makes writing good that

surprised you and so like it's gone

through its understanding of itself but

it's asking me questions all along the

way to like really figure out concretely

what are what the hell is it that I'm

actually trying to say Um, and this is

like super useful cuz I think a lot of

the times when I've gone to Old Spiral

and other AI tools to like ask for

something, it may ask questions, but

usually you have to prompt it to ask you

questions. Um, and the questions don't

really help me really think about what

I'm saying that well. We had a we had an

early user who said something about

Spiral. He said that Spiral helped him

go downstream from the original thought

that he shared with Spiral as opposed to

it just being a repurposing of the

original thought that he shared. And I

think that like put it really like well

and succinctly of like Spiral will help

you take that initial like jumble of

thoughts that you had and go a little

bit further with it um from these

questions which I think is like super

useful in figuring out what it is that

you're trying to say. Um,

and I guess it's also like seeing it

from different perspectives also helps

really like understand the shape of the

thing that you're trying to talk about.

Um, yeah. So, I monologued into it a

bunch of stuff and then sort of like

helped me like think through the

different things that I wanted to talk

about right now. Um, so that's the

interviewing part of Spiral and like

sort of figuring things out.

>> And just I just want to um I just want

to like pause you there. I I think the

interview thing is an interesting thing

and it's it's one of the things that we

came to very early on in this process

because um we were thinking about oh

yeah how do you build a how do you build

a writer with taste and um

one of one of the metaphors that we use

to think about this is how would a

person do this and I think that's a

really good and important metaphor for

building AI products is if you're

building an agent first you have to

think about like

how does an actual person do the task

that the agent is going to do and So,

um, you know, I was thinking about for

me in my in my writing, um, how how do I

ghost write for people well and and in

order to do that well, I would need a,

uh, I need to get a sense of who that

person is and like ask them questions

about it. And there's this like, you

know, mutual discovery process. and you

took that and and like built this

basically like interview mode, which I

think is is one of the things that um

makes this really different is it's not

its job is not to just spit out the

answer right away cuz that's how you get

slop. Um it does spit out an answer if

it knows who you are and what you want

and all that kind of stuff because if

you've built a relationship with a ghost

writer, for example, and you're doing

something like what you've done before,

that is actually can be a pretty quick

process. But um otherwise a good ghost

writer their job is to um make you feel

seen like really get underneath the

thing that you're trying to do and and

that's how you make that's how you ghost

write something that feels like it came

from the person and isn't just like some

generic [ __ ]

>> Yeah. Yeah. And I think LM are and and

this actually goes deeper into the story

of how else this product works, but LM

are great at doing that. just sort of

like helping you see things from an

infinite number of like perspectives.

Um, and I think one of the things that

we spend a lot of time with the prompt

for the interviewer inside Spiral is

like yes andinging you sort of, you

know, coming from like improv is it

always kind of has to like provide value

in the questions. Like some questions

are kind of annoying and it's like you

know if it's not providing value and we

try our best it's like yes and um and

like it it is giving me a lot of

questions like how like you know how do

you encode asking good questions to the

prompts which is kind of what you just

asked. Um, and so like it's thinking

through all of the things. And to your

point of like slop, one of the things

that we were very intentional on of like

we're going to use a reasoning model and

we're actually going to show the user

all of the reasoning and you can see it

all because you know we we're calling it

a writing partner. And I think that word

partner is really important and I think

it's kind of one of those like

principles that we've anchored

everything around. And with like any

good collaborator or partner that you're

working with, whether it's writing or,

you know, engineering or design, um,

understanding the perspective that

they're coming from and how they're

thinking about the thing, um, helps you

understand like, oh, this is where

they're thinking about it. This is where

we're um, here's where there's a delta

between how I'm thinking about it, how

they're thinking about it, or like, oh,

they've got it completely wrong. They

they've understood something incorrectly

or differently from how I have. And it's

really useful to be able to like look at

the thinking. Um I you know when you

start a conversation with spiral the

thinking is open by default. You don't

have to click it open. Um as opposed to

you know other AI apps they do show you

the thinking but you have to click it

open. I think that's a very small tiny

intentional thing of like we want you to

read it like actually understand what

it's doing because if it's doing

something you don't want it to do you

for people to see that and you can just

tell it. Um, yeah, I think that's super

important in

building something that everybody else

is trying to make content like here's,

you know, even the old version of

Spirals, like an infinite number of, you

know, it will spit out an infinite

number of things at a click of a button

and I think we've gone completely the

opposite way. Um, where we've kind of

zagged and been like, let's go slowly.

Like, we're going to use a thinking

model and it's going to think for a sec

for for quite a few seconds and slow

down, be a bit more intentional, answer

the questions. And sometimes it's

annoying. Sometimes the questions are

annoying. Um, but sometimes and like

more and more and more the questions are

really helpful of being like, "Oh, yes,

that's exactly what I meant." Or it'll

ask me, you know, he'll ask me a

question and I'll be like, "You're way

off, dude. And here is actually what I

mean." But then that helps me articulate

what I actually mean because you're

asking me this question that's like way

over left field.

>> Totally. Um, okay. So, so I think we've

gone through the interview stuff. Now,

show us once it's gotten us sense of who

you are, what you're trying to do. um

how does it you know we really focus

this around short form content so like

tweets, LinkedIn posts, emails um uh you

know other types of like short form

marketing type stuff. How does it um how

how do you actually make something with

this once it's interviewed you?

>> So usually when you when you start with

it you'll most people will say help me

write a LinkedIn tweet expost you know

um email blah blah blah. And usually by

the time you've answered quite a few of

its questions, it will just be like,

"Great, I'm ready. Let's start writing."

Um, but in this case, I'm just going to

say, "Hey, help me write a banger tweet

about this, even though I started with

saying I'm doing a podcast." Um, and the

first thing that it's going to do is

reason over all the things that you

asked it that you've like replied to it

with. Um, and then one of the things

that we learned sort of in the journey

of building this is this needed to be a

multi- aent system. like having one big

model do both the interviewing and the

writing just really didn't work. Um, as

we tried to layer on more functionality,

it just started to break because of

context rot. You know, all of these labs

are talking about 1 million context

windows, 1 million token context

windows, and it's just like that's yes,

it can do that, but how well can it

actually pay attention to all those

tokens? It just clearly isn't there yet.

Um, so we have a handoff system where

the interviewer gives it to the writer.

Um, but actually they share the entire

same context window. So it's not a

summary of the previous thing. It's not

like a tool call where the interviewer

is summarizing the conversation to the

writer. Um, we're literally taking the

previous context window and just write

into a new system prompt. Um, oh, sorry,

write into a new system context window

just being like, "Hey, here's your

previous chat." Um, and we do some

prompting to make the writer like

understand what's happening like, hey,

you're coming from the interviewer and

that kind of stuff. Um, and so like we

just saw here, it did a bunch of

thinking. And the first thing that it

did is it spat out three options and we

have this UI where you can kind of

explore all three right in one go. Um,

and it's thought a lot about what we've

said. Um, and the reason that we did

this is I think when we first started

down this process of creating a new

product, I think you were really really

interested in the idea of exploring the

sort of infinite canvas of possibilities

that LLM could give. Um, and this was

like something you were really really

excited about and like traversing up the

tree and then like branching from the

tree

>> the tree thing.

Um yeah, like March 2025 was a lot of

trees and thinking about trees. Um I

actually vividly remember um I think it

was like it was like a Thursday or a

Friday and I was in our old office in

Manhattan and you just like sent me a

[ __ ] tree on Discord. Like it was

like here in Brooklyn. It was probably

like in Fort Green or something. You

just sent me a picture of a tree and I

was like this [ __ ] we were really

really interested about this idea. I

mean it's like it's true, right? because

they can just produce an infinite number

of tokens and you can kind of explore

all these different ways of of saying

the same thing. Um I think what we

realized is

one from the same context window every

single iteration that you ask it for it

just degrades in in quality. Um and we

even see that still today with Spiral

like we're still not there yet with like

its writing ability. Um, but there is

still something really interesting there

about this seeing things from different

angles and sort of pruning and being

like, you know, or recently I've been

saying like chiseling marble. Um, you

know, you you start with this like blank

slate. Maybe it's not the best analogy

because you have these whatever. Um, but

you kind of like pick and choose what

works and what doesn't work. So, it

starts off in this UI, you have three

options. Um, you know, and very quickly,

you know, it gives you sort of titles of

like what is the the general gist of

this version. So this is like an honest

realization. This one's more about the

failed experiment. And this one is like

questions overdrafts. And you know, I

think we spend a lot of time thinking

about how do you actually um interact

with these three options cuz chat is one

thing. You know, I can come in here and

I can like, you know, type um but also

we kind of want the AI and you to be

able to interact with all three of these

things. So right now, Spiral can read

all three of these things. it has

context of what's open. So like if I

close one, it knows that there's only

two open right now. Um if I like, you

know, close all of them, it'll

understand that. But also if I make

edits, it understands about the manual

edits that I've made. The other thing

that we can do, um is you can just come

over here and you can just highlight

things, you know, like I can say, you

know, here's a bunch of stuff across all

three that I, you know, I don't like

these. um

remove them and I can still

collaborate with spiral across all three

drafts. It doesn't have to be one draft

at a time. Um and it kind of just like

allows me to go and traverse the tree.

Um sort of but just three at a time like

I don't there's only so many that I can

handle mentally. It's also text. I think

one of the one of the things that we

learned over the last like six, seven

months is

I think everybody is really used to from

tools like Midjourney seeing multiple

options when it comes to image

generation, but text generation like

multiple options is really hard to pass

what the hell the differences are. Like

you can tell, okay, the middle one here

is bigger, but like is it that much

better? And so three kind of felt like

that perfect um middle ground between

showing you multiple options but not

overwhelming you. Um, so yeah, that's

how it works. And typically when people,

like when I use it, when I've noticed,

you know, a lot of our users using it,

they sort of start here, they, you know,

they'll find one angle and they're like,

"Ah, I don't want to go down this angle.

I want to go down these two angles."

>> I like the first one, the questions is

greater than than drafts. I think that's

actually um the the the the hook

actually got me,

>> right? And and so like that's typically

what happens with people. They use it.

They're like, "Ah, that one didn't get

me, but that one got me." and they'll

like go they'll find bits that they like

and they'll bring them over and then you

know couple of more conversations back

and forth and they'll find something

that they're like pretty happy with. Um

so yeah, that's that's generally how it

works. Like it's we're calling it a

writing partner. A big part of that is

that it talks to you, has conversations,

ask you questions, um then it drafts and

it's just like very helpful in drafting

uh across three different things. you

can um you know I can ask it for hey

give me a bunch of options for the hook

um and we have this UX where it'll

generate a bunch of different options

within one specific draft and you can

kind of very quickly um

change them and switch them out and see

what works and what doesn't work. Um

and we've like taken a lot of

inspiration from cla code. So, it has

all these tool calls of like it reads

the draft. Um, and then it'll like then

make edits. Um, so like I built an

arrow, so like I got next, here's

another one. Here's another one. I can

always go back and see the original.

Here's another one.

>> Oh, interesting. I this I've never even

seen this. This is really cool.

>> Um, yeah,

>> you're building so fast I can't even

keep up.

>> Um, yeah. And so like there's a lot of

tiny little moments like this that we've

added that like help make it sort of

useful to do that. Um that's kind of

currently where it is like we have you

know like we talked about workspaces. We

have writing styles which is very much

grounded in what old spiral was. You

know you give it a bunch of examples. It

tries its best to emulate those that

writing style. And again, I think one of

the things that I think I found over

this experience is that

writing is something that is fluid. Um,

I think when we first started this

experience, one of the things that I did

actually was I read this book. Um, I

read why I write by George Orwell and

this was written uh during the Second

World War. When I was reading it, I was

like, "This writing kind of sucks.

There's some parts of this that I don't

like I didn't like." And I think that

made me realize how much the like the

way that we talk, the way that we

communicate changes so much. And so like

good writing is so fluid. Um I think

that's something that I've had to come

to accept. And

you know, we're not training our own

model. I think we, you know, we kind of

like how far can we push frontier

models. um and just with prompting and

sort of context management and like sort

of economics and doing handoffs and that

kind of stuff. And there's so much of

their training data that is slop. Um and

that is something that I think we're

we're like it's interesting. I I

remember when we first started this, I

was going down this very much like

rules-based approach. all of my

prompting had these like rubrics with

scores and I would ask the the writer to

like score itself of how well did it

write a hook. Um and none of that

worked. Um it was all really like it was

all really bad. And I think we decided

to move more into like let's allow it to

be fluid. Let's allow it to like be

flexible like let's not give it let's

not constrain it with these really hard

rules. Um and that

plus sort of the most recent generation

of models sort of claude 4 GBT 5 they

unlocked a new like level of

capabilities um that just wasn't

possible for especially for writing

especially for like interviewing well

and reasoning over a long context window

and

yeah

>> yeah I mean I love all that I think that

the thing that's in my head about this

is just looking at this and listening to

you talk, I'm just like I'm so proud of

you. um because where you were 8 months

ago is so different and where the

product was is so different and you've

just taken this thing and completely

reinvented it and it's um and in a lot

of ways I think you've reinvented the

product and I think you've I don't know

if you've reinvented yourself

necessarily but you've you've certainly

pushed yourself in this new direction um

to really understand what makes writing

great and it's been just awesome to

watch and what I I want people to see is

where you started. So, could you open up

old spiral so we can show them what that

looked like so they can get a sense for

like how different it it was?

>> Um,

and interestingly, this is not the first

version of Spiral. This is

>> I guess that's true.

>> Yes. So, the first version of Spiral was

AI generated slot by Dan Shepard. No,

I'm

>> It was

That's my specialty.

At every we run four AI products

in-house and we just launched a new one.

When we needed to build a beautiful

landing page for it, we used Framer.

Most design tools lock the good stuff

behind a payw wall. Expensive and

definitely not fun. Framer is different.

Framer already built the fastest way to

publish beautiful productionready

websites. And it's now redefining how we

design for the web. With the recent

launch of design pages, a free

canvas-based design tool, Framer is more

than just a site builder. From social

assets to campaign visuals to vectors

and icons all the way to a live site,

Framer is where ideas go live from start

to finish. Your entire workflow lives in

one place. No Figma imports, no messy

HTML. Design your end result in Framer

and it turns into a working website

faster, cleaner, and more efficient.

Because Framer is a true design tool

that also publishes professional

production ready sites, you get features

like P3 color spaces, SVG animation,

inline editing, and precise scaling.

It'll help you stand out as a

professional. Ready to design, iterate,

and publish all-in-one tool? Start for

free at framework.com/design

and use the code DAN to get a free month

of pro. Rules and restrictions may apply

as always. And now back to the episode.

>> So, this is the current or the previous

version of Spiral. It it got a bit of a

facelift when I first joined um about a

year ago or so now. And

what it does is you will feed it

examples. you would feed it examples of

writing that you liked and then you

would give it a new input and it would

try its best to recreate that examples

that you gave it. So, here is one uh

rough outlines into punchy short tweets

is one of the spirals. And the way that

it worked is we just gave it a bunch of

these examples of things. Um like look,

Japanese population is collap Japan's

population is collapsing. Um and what it

would do is it take all those examples

and it would create like basically a

style guide. Um and then you know the

prompting was very simple. You would

just give it um

you know let's take maybe just the

prompt for that. Let's see what that

does with that. Um, and then it would

just like generate three. Um, and there

are people who still use this today. I

think there is like certain parts of

every that still uses this for like

summaries and bullet points of things.

Um, and this allowed you to very quickly

get some options. Um, I think what was

interesting to me

was that this was a great first start

for last year. Um, I think that's why a

lot of people kind of gravitated toward

this. It was like, "Oh, it's made well."

Um, what it what it said that it did, it

it definitely did it. Um, but I think we

would look at the outputs that came out

of this and we're just like, uh, it's

not there. And then this paradigm didn't

allow for collaborating. Um, and I, you

know, I tried to hack it a bunch of

ways. Like I had

>> collaborating with the AI specifically.

>> Collaborating with the AI. Exactly. Um,

and you know, I tried to add on a bunch

of things. You know, a lot of people who

still use this today will like write

custom instructions and that's where

they will try and like push the AI

towards where they're actually trying to

go. But then it never really worked. Um,

yeah, I think we we saw usage declining

and like personally for me, I

I never liked the stuff that came out of

it and I never found myself going like

reaching for it ever for anything. And

when I did, it was cuz I was like, I

should be if it very much felt like a

should. I should be using the product

that I'm claiming to to wanting to work

on. Um, but as I've learned over, you

know, the last couple of years, shoulds

are not what I should actually be doing

with my life. Um, and

yeah, I think it was

it was also this like interesting

tension of there's people who actually

use this thing. There are still people

who use this thing today. um which is

really cool, but I I just I'm struggling

to care and we're not using it. And I

think one of the things that has become

very apparent for the product studio

over the last couple of months is if

we're not using it, it just doesn't

matter. It doesn't matter if other

people are using it. I think, you know,

Naveen in Monologue is such a a good

example of that. Like he just hammered

away for months until he found something

that he was really excited about and

everybody else was excited about. and

then he hammered away until it actually

worked. Like I was actually I think out

of everybody at every I was the last

hold out to use monologue cuz he just

wasn't there yet. Um and then and then

and then he got me and like and I think

that's one of the things that we've

learned at the progress studio is like I

think if we're super excited about it

and the people around us are super

excited about it like let's just do it

and it's going to be great. Um yeah and

this I just don't I don't think any of

us were excited about this anymore. Um

>> yeah. Yeah. I think there's a couple of

lessons in here for me is one of them is

yeah like when when I first built the

first version of spiral it was very cool

and

but things move so fast in AI that

because it it stayed with this sort of

like text transformation thing as soon

as the models got better it needed to be

totally different and we just didn't do

that basically and and and so that's one

thing another thing is Um,

it it really underscored for me how

important a daily use product is and it

made it forced me to think about

um,

well, how useful is this sort of text

transformation for a writer and how

often am I going to need to do that? and

um

and and also how complicated it is to

actually produce really good content and

how hard it is to do that in a oneshot

way and that we needed something like

more dynamic.

But I think the the biggest thing is um

I built the first version of this and

then handed it off to you. And this is

something that we've seen a lot actually

now. We've seen it with you. We've seen

it with Yash, who um is building

Sparkle, which is something that I built

in like 2020. Um and uh there's this

process that a GM has to go through if

you're working on something that's not

yours to like fully own it because

um I think you know seven or eight

months ago, whenever that was, you came

to me and you're like, I don't know if

I'm the right guy for this. Like I don't

know if I should be working on this. Um,

and you were just banging your head

against the wall being like, "I got to

get the MR up." And

and um

and that sucked to see. And when we dug

into it together, it was clear that

um

you didn't have a vision for it because

it didn't feel like yours.

>> Yeah.

>> And then that was really demotivating.

And for me, I was like, well, I mean, I

have a thousand like I can give you a

bunch of stuff, but like I don't think

that solves the problem because like I'm

not the GM. like you have to you have to

solve the problem and I can be there to

support and help and whatever and

um I sort of put it to you is this like

what you want to spend your life on like

do you want to work on this or do you

want to do something else because if you

want to do something else one of the one

of the lovely things about about what

the setup we have at every is you can do

something else like you don't it's not

like uh the funding runs out and then

you know the startup fails it's like

okay this didn't work great let's do

something else um let's keep

experimenting. And I think you went back

and thought a lot about do I care about

this problem? Do I really want actually

want to make great writing or I think

what you ca what you came to is the

thing that motivates you is the is the

great writing bit and the text

transformation for marketing was not not

that interesting to you. Tell me about

that.

>> I think yeah the journey was really

interesting. it,

you know, I think it was March. Um, I'd

spent

January, February, and March just like

dragging myself out of bed, just like

feeling guilt. And yeah, we had that

conversation and I think the thing that

I realized was that good writing is

downstream of good thinking.

And that is a really interesting problem

of everybody is going towards let me

just generate a thousand things, make a

million blog posts, put out a bunch of

tweets, um you know AI generated

replies. And it one I just think is like

where do I want to what do I want to do

myself? I think I want to be intentional

with what I write. I want to be

intentional with what I think. I want to

be intentional with what I say. And but

that doesn't mean that like I have to

shun away modern tools. I have to shut

away this crazy we've made sand think.

I'm not going to say no to that. Um

but like how can I use it in a way that

it actually helps like yes and me or

like you know gives me that you know

it's

the sum are greater than the equal than

its parts or whatever that phrase is you

know um and I think that

>> the whole is greater than the sum of its

parts.

>> That's exactly what I was trying to say.

Um

you know Dan Shipper steals hats and he

steals words out of my mouth. Um, we can

cut that.

>> I think I said that in our Q4 Q4

kickoff. So, who's stealing from who?

>> That's fair. That's fair. Um,

and yeah, I think just like I

think there's something really powerful

with

good writing or like, you know, it's not

even about good writing.

articulating what you want to say to the

world well can do so many things. Um,

you know, not to like go all crazy and

be like this changes the world, but like

good writing, good thinking, good

articulating moves mountains. It moves

people. It gets people to do crazy

things that they think is like

impossible. It gets people to, you know,

shun the status quo. It gets people to

like, you know, do all sorts of things.

And

I worry about a world where people are

like, "Ah, cool. I'll just let Chat GP

do all the thinking for me. I'll let

Claw do all the thinking for me. It's

like no, like it's actually just let's

use it intentionally. And I think what

that requires is, you know, if you think

if you go back to the first podcast that

we did, I actually said this where it's

like most people aren't good prompters.

99% of people who who are using AI are

probably rubbish prompters. Um, and I

still think there is this beautiful

space for building products where it's

like, hey, don't worry about the

prompting. um just like use the product

well and if the product is designed and

prompted well to do the thing that you

know I think it should be out in the

world which is taking a beat and

actually thinking about what you're

writing AI can help you do that but I

think the models as they are people the

path of least resistance is just give me

this just right don't make me think um

and I don't I don't think we should

exist in that world um and I I think

deep down people don't want to exist in

that world and AI. We don't have to be

lit. We don't have to shun away AI to to

to still have that world. Um, we can

have both.

>> I totally agree. Um, and I think that is

a really natural segue into how are you

building this? Um, and what are the

things that you're using to make such a

polished product, fully featured

product, useful product as one engineer?

>> Yeah. Um, it's, you know, to your point

of the models just change so quickly,

the tooling changes so quickly. Like

if I think back to where we were in

March when we first started this, I was

I was probably bouncing around from

cursor to windurf and that was it. And

>> windfur was the new thing. It was like,

wo, this is actually it was the first

thing that was a little bit agentic.

>> Yeah. Yeah. They had their like cascade

feature. Um, and Cascade was so

different and it was like agentic and it

would do all this stuff. And now I'm in

my terminal all of the time. Um,

I, you know, the one thing I regret is

learning how to use Vim because I never

use Vim anymore. I'm never like bouncing

around a text editor in Vim anymore. Um,

shout out to all the people who know how

to use Vim. Um, and so now I'm

completely inside my terminal. For a

very long time, it was all cla code. And

as people who know about every, we all

love claw code. I think cloud code was

that first thing that like kind of got

rid of the fluff. You don't need an IDE.

You don't need all of this. It's just

like text input, give the agent a bunch

of tools, which and it's actually not

that many tools. It's read and edit. Um,

which is like kind of all it did to

begin with, and like make to-dos for

yourself. I think that's the first three

tools that Claude Code probably had. Um,

and that's

>> and use bash, right?

>> So, four things. Um, and it was amazing.

the models itself were really good and

then Claude I think I think there was

this moment at least this year uh who

knows probably by next year it's not

going to matter but Claude 4 I think for

me feels like such a pivotal moment in

this space because I think it just

unlocked so much capability in

programming writing and like a lot of

things um

I guess the other thing to say is like

that also opened up the space for

everybody else to try this so there's so

many CLI tools now we AMP OpenAI have

their codecs and most recently I've been

using factories Droid um and I've

completely fallen in love with Droid and

one of the reasons why and we can jump

into it and I can share my screen but I

think one of the reasons why is that

Droid is model agnostic almost they give

you five models to play with. They give

you GBC 5 and they give you all the

anthropic models and then they most

recently have the GLM 4.6 six model. Um

the other thing that I think they've

done really well is they've really

thought about the ergonomics for each

model. Um they're clearly prompting it

differently. They've really thought

about like, okay, we're only going to

support this model and how the models

interact with each other and the

ergonomics work really well where I will

literally have the same type of request

inside cloud code and another one inside

Droid and Droid will just do so much

better with the same model. It will this

is I'll give you a very concrete

example. Um, I had a database migration

that completely [ __ ] up and I was

actually in a car going to IKEA to New

Jersey. Um, so I was in the car going

over the Williamsburg web

>> New Jersey, not Red Hook

>> towards my wife is she she she ordered

the thing at the wrong place. Um, and so

yeah, she's driving this car going over

the Williams Bridge. I'm there in the

passenger seat just going insane to

Claude Code being like, "This database

migration is fucked." Um, and then I

just took the same thing over, same

model. It was Opus 4.1 and it just got

it three minutes. I was like, "Oh,

here's the issue. Got it." Um, and so

they've given it they've done a better

job that I think than Claude or the

anthropic team have in like giving the

model the ergonomics and the tools that

it needs. It's like, oh, here are all

the tools that I need and here's like I

think the right prompting. I don't know

what else they're doing, but there's

something about it where it's like I

feel so much more confident coming to

Droid um that I just don't feel in some

of the other tools. Um

I think the other thing about

what your original question was like how

did I build this as a single engineer?

Um I think also I always get shocked

every time I talk to other people. Um,

we at every

we're clearly at the edge of how to use

these things. Um, I think for a very

long time I was the number one user of

Droid. I've been ripping the most tokens

and it like surprises me. So like I'm on

their max plan and it was October 3rd to

October 24th or something like that.

I've already run out of tokens. Like I

ran out of tokens well before time. Um,

>> we got to call Ben Tossel and get you uh

>> get me some more tokens. Yeah.

>> Um

because yeah, I think we're just like

ripping them so much and

I think going into the deep end there's

a lot of people for maybe not now, but

like the last time I spoke to people,

they weren't they wouldn't use yolo

mode. They were afraid of using yolo

mode. And I think that's kind of a

foregone conclusion for us was like of

course you're going to use yolo mode.

Why are you going to accept everything

that it you're just going to let it do

it? Um

I think really leaning in. I think

letting go of the identity of I'm an

engineer and I'm gonna write I'm gonna

use Vim and I'm going to write code and

blah blah blah. Like letting go of that

identity and being like I'm when you

really peel back the layer of what I

think I am is that I'm somebody who just

makes things and I don't really care how

I make them. I do I care about like how

it's built and like I clearly care about

the end product, but I don't care if I

have an agent writing the code or if I'm

writing the code. As long as it's good

code and it does what I want it to do,

does it really matter? You know, um

>> but how do you do that? Because I think

a lot of people listening or watching

are saying, "Cool, great. But how do you

actually be a craftsman if something

else is coding for you? Isn't that all

just going to be slop?"

So, let me

open up my

terminal

before before I share my screen. I think

it's a philosophy of, you know, you

brought this up right at the top of

measuring twice and cutting once. And I

and I think this is actually the one

thing that I've learned from from like

trying to build something that writes

well, which is also just like looking at

something and feeling internally what

actually like calls out to me and like

does that feel right or does it not feel

right? And I think you could just you

could do that with with a button. Like

does that button feel right or not? Does

that spacing on that thing thing feel

right? Does the does the not does the

code feel right, but does the thing that

I expect the thing that Claude just

ripped out, does it do it the way that I

wanted it to do it? But also further,

does it does it add to the piece of the

puzzle in the right way? Like LM can

skin a cat a million different ways.

There are probably only a handful that

you actually should be using that are

like right or like actually fit the the

the way that you want it to do. And so I

think a lot of like building this

product has been um feeling out like

okay when I ask Lord to do something or

one of the other models does it actually

do it the way that I want to do it. Um

so oh if I share my screen um a lot of

the ways that I use and and you could do

a lot of this in in cloud code as well.

Um but a lot of the ways that I use

these tools

is one I'm always doing multiple things

at once. Um, I think waiting around

for the model to complete and I think

the worst habit that I got into that I

had to like uh cut out was scrolling

Twitter whilst I waited for an agent to

complete. Um, that is a just killer to

productivity for me. Um, so and what is

it that you do because you do a thing

that I actually started to copy for

this. What how do you how do you stop

yourself from scrolling Twitter?

>> So word trees is is like the big thing.

So

>> Oh, no. I was saying, um, so what what

I've noticed you do is you take your

phone and you put it in another room.

>> Yes.

>> And so I started putting my phone next

to yours.

>> I know it's confused whose phone is.

Yeah. So we we have we have a couple of

places in the office where there's just

a couple of ledges or we have this

beautiful every sign. my like I think to

talking about like in intentions is like

one of the things that I try to do the

moment I walk in is I take my phone out

of my pocket and I just place it face

down on one of these ledges. Um and I

leave it there and I try my best not to

look at it. Um and I also I'm always on

do not disturb as you can see here in

the top right corner. I'm on do not

disturb. I have notifications off like I

have Apple Watch but like this notifies

me about nothing. Um, and I think it's

it's really easy to be like, oh, now

because of this tooling, I can do the

job of three people as one person. And I

have seen in myself and in other people

that actually what people end up doing

is they end up doing the job of one

person and then they just procrastinate

or they do the other things in their

life that they think they want to do.

And I I noticed in myself that I wasn't

happy doing that. I really am somebody

who's allin and then when I'm if I'm

working, I'm working. Don't talk to me

about anything else. My wife hates that

about me. Um, and then if I'm not

working, I'm not working. Like I'm

trying to be present, intentional. If

I'm with somebody, I'm not texting other

people at the same time. And that's been

really helpful. And I think it was also

really important when working with like

LLM models cuz like programming before

was I would look at the code, I would

spend a lot of time, attention reading

it, I would read it, I would read it,

I'd read it, tests would pass, I would

spend a lot of time. And now it's like,

no, I'm just going to let it go rip and

I'm going to go do something else. Um,

and so what I think that's allowed, and

it requires being really intentional, is

just can I do parallel things? Like, can

I just do multiple things at once? Um,

and so one of the ways that manifests is

I'll in in one terminal tab I'll have

like two panes or sometimes I'll have

like three panes open. Um, and I'm

usually like ripping Droid on like a

bunch of different things. Um, so like

here, this one I was fixing a bug and

like you know in this one I might be

actually earlier today Kate gave me some

copy edits inside the app. So I had

Droid go through and like do those copy

edits. But then here in another window

I've actually been messing around with

some of our judging prompts and I've

been using like Python notebooks. So I'm

like writing out Python notebooks with

Droid. Um I have this other one here.

>> Obviously the the judging prompts are

like uh uh judging whether writing is

good. And that's a sort of core

component um of making good writing is

is getting the AI to recognize if

writing is good or not.

>> And I think to to

go on a quick tangent really I think the

one thing that we saw about Claude 4

Opus specifically uh back in June or

July or whenever it shipped um it's

really good at judging writing and not

rubber stamping. I think models before

this would you would give it some of its

own writing and it was like yeah my

writing is great it's a B+ or you would

give it other you know other people's

writing or other agents writing and it

would always be like a B+ and there was

a lot of like you know tricks that

people would do like give it a rubric

and then tell it that actually a seven

out of 10 is impossible forcing it to

kind of like actually change it from but

then everything would just be a six or

like a 5.8. Um, and what we found was

that Opus was actually really good at

reliably, repeatedly um, giving boolean

answers and like good like uh, pros as

to why it was saying yes or no for

something. So we we have this one test

internally where we see if writing is

like engaging. Does each sort of

sentence keep a reader engaged as you go

through? And we found that like Opus was

the first Opus 4 was the first model

that would like reliably

almost every single time would say the

same answer and it would say the same

answer that we would agree with. Um but

then also came over to Sonic 4.5 and

somewhat now to HighQ 4.5. Um yeah, and

that's like a really important part of

the product, but it's also really

important for us building this product

and for me building this product of like

where is it that this needs to get

better? Um, and this is actually a

feature that we're going to build

directly into the product. So like the

Spiral can kind of like judge itself

using another agent um to see where else

it can improve. And this kind of helps

us create this like feedback loop of

being able to watch how good or bad are

the um outputs that come out of it. Also

with people just telling Spiral, I don't

like this. I think it's really the

benefits of having, you know, the one

thing that we didn't have from the old

product. The old product only had thumbs

up and thumbs down and that was really

useless. With a chatbased product,

people just say I don't like this

because and you know we have um we're

using something called raindrop which is

basically like sentry for AI agents and

we just get a report every single day of

like this is what people said they liked

and this is what people said they didn't

like and here's why and I can just see

the chat and it surfaces it all up for

me. So being able to see how people use

the product and just read what they're

saying to Spiral is really helpful in

improving it. Um,

>> and and one of one of the interesting

tangents that we've been down over the

last couple weeks is, and this is one of

those things where I only ever read it,

so I don't know if this is actually the

right way to say it, but Disp and Jeepa

is how I say it in my head. For other

people, maybe it's DSpie and Gipa, I

don't know. But basically there's this

um there's this prompt optimizer uh disp

uh that is sort of like having it's

having a moment right now among AI

people uh where it will um basically you

can give it a prompt and it's and like

an objective and it will it will

optimize your prompt for that objective

and Jeepa is a specific method for doing

that that I can't totally explain but

it's a new and interesting way of doing

it and so one of the experiments that

we've tried is giving me a bunch of um

tweets and then having me thumbs up and

thumbs down them and then give like

concrete feedback to see if we can

generate a judge that has my taste. Do

you want do you want to talk about that

and what what we found? What's worked

and what hasn't worked?

>> Yeah. So, we we have a great piece on

every with Michael Taylor around Disby.

He's actually the one he he came here

from the UK um and sat in the office and

like actually taught us how he uses it.

Does he say Disp?

>> Uh, I don't know, but I I think it's

Disp. I think Disp sounds so much better

than DSP. Um, even though I guess like

DSP is probably the right way to say it.

Um, and yeah, it it is just like kind of

when people look at it for a very long

time this year, I've been going on their

landing page and I'm like, I have no

clue what this thing is. and he did a

really good job of explaining um and I

think we have a video out where he's

actually teaching me and Kieran how to

use this um where he finds just ignore

80% of what it is and just use it as an

optimizer. And when you just use it as

an optimizer, it makes so much sense. Um

you don't have to throw away all of your

like frameworks that you might be using

already in your like app if you're using

the AI SDK or whatever. Um just use it

as a promptizer, extract the prompt, put

it in your product. um because it's very

much built to be this like uh

all-encompassing framework, but it's

just really confusing. Um I find it to

be. But yeah, using as a prompter is

great. And what we I think we can

actually like look at that um that judge

that we created for you. Um, the way

that it works is you basically just give

it a bunch of uh predefined labels with,

you know, generate a tweet about blah,

and then we had you literally give it a

thumbs up or a thumbs down and then just

write out your thoughts. And we would

just start with a basic prompt to begin

with, see how good did it do. So like

the base prompt was about 58.8% accurate

to you. Um, and that was just the prompt

that I asked Claude to come up with. Um,

so like just over half of the time it

would say the same thing that you would

say.

>> It's not terrible, but terrible. Yeah.

Yeah.

>> Yeah. Um, but like you know

you could flip a coin basically.

>> That's true. I guess you're totally

right. Yeah, it is terrible.

>> Especially if you know this is just for

writing marketing materials, but like

15% like if you're trying to do

something important, this is really bad.

Um,

>> yeah. Yeah. Yeah.

>> And the way that it works is then Ga and

I I also can't speak to exactly how it

works, but my understanding is is that

you you give it like a metric for how it

can what it will do is it will generate

a new prompt and then it will test that

prompt against um your your judges and

the data that you've given it and it'll

see how close does this new prompt is

generated get to the sort of like gold

standard which is from you in this

example. um and then goes, "Okay, this

prompt that I tried wasn't that good, so

let me try another prompt." And so the

way that I kind of see it is that it's

just like trying as many prompts as it

can until it finds something that's

better. So it really is just sort of

like throwing spaghetti on a wall and

seeing if it sticks. Um and it, you

know, it will rip through tokens a lot.

Um you you can basically set it to a max

number. So here I have like max five

full evals. So it goes through five

means it goes through 85 iterations. I

don't really know what that means, but

whatever. Um, and so like here you can

see it's just like like here's one of

the prompts it came up with and then it

came up with another prompt and you know

that prompt was 66.7% accurate. Um, and

so like it keeps going until it gets a

little bit better. You know, here's

another one that I tried that was 33.3%

accurate. So way worse. So the idea

>> do the inverse of that prompt and it

would be

>> exactly

>> um and so like it's just you know it it

learns and it try and it learns from

trial and error which I think is cool

which is when I think about how I was

prompting for very subjective things

like this um it was just kind of trial

and error and I would work with Claude

and be like okay the output kind of came

out like this but it it it was missing

this thing give me a suggestion or you

know one of the things that I would do a

lot before this and I still do this is I

would find other people's prompting

guides and I would say to Claude distill

down the principles of these prompting

guides and then help me and like that's

somewhat useful but I think this at

least for creating judge prompts has is

a bit more it feels a little bit more

scientific maybe it's cuz we're in a

Python notebook and there's percentages

and stuff um but it's also allowed it to

be like oh I can very clearly see the

before and after um so then finally it

spat out this prompt that was 76.5%

accurate which which is a lot better

than half. Um, again, not perfect, but

we're getting closer and closer there.

Um, and then it like sort of goes

through it and we can actually use it.

Um, so maybe what could be fun is if I

stop sharing my screen. Um,

>> wait, wait, wait. Before before you go

there, before you go there, I I just

want to know what is what is this be

Dan? What is in the prompt? Like, give

me a couple things that are in the

prompt because I I'm just one of the

thing that's that's really cool about

this is it's telling me stuff about how

my brain works that I might not even

know.

>> It's actually very simple. Like if you

asked me to write a prompt like this,

like write a prompt that emulates Dan's

judging, I would not write something

this simple, which I think is kind of

counterintuitive. Um, and and this is

it's just a simple list of things. Um,

and and I don't think this is perfect,

right? And it doesn't work all the time.

Um, and like if I was to say, you know,

in the in the data set we gave it, it

said 76%. I feel like it's probably more

like 65% um accurate to what like I I

would perceive as actually good content

versus bad content. Um so like it

definitely still needs some work, but

like it's some very simple things like

simple concrete specific language, not

vague generalities. And I think what's

really interesting about this is

past generations of prompting would be

like really really specific on the

prompt of like well what is simple

concrete specific language? And it would

have like here are like a bunch of

examples of what really simple concrete

specific language is. Um and what we've

seen from doing this process is actually

just letting the model breathe and like

you know what they're big enough. They

have enough parameters. They have enough

training there. They clearly know what

simple concrete specific language is.

Maybe sometimes it's maybe a little too

buzzworthy and so there's like stuff

that we have to do there. But it we the

models have gotten a lot better. Um you

know clear promise that gets paid off.

you know, I actually think that's a

pretty important one if I was to say

like the kind of feedback that you've

given me is is, you know, a lot of that.

Um,

relatable, sometimes funny, um, focuses

on topics that people care about, the

proper tense, one clear idea, but like

what is bad content? Uh, it's generic

openings that could apply to anything.

It's the wrong tense, it's too wordy,

breathless tone, um, chop slop, unclear,

choppy syntax, telling instead of

showing. So there's a lot of like

there's like some specifics here like

proper tense but it's also principles of

like telling instead of showing. Um I

think the other thing that's interesting

about um the uh this prompt here is like

sort of the evaluation approach like how

it actually does it starts with a hook.

Is the opening sentence surprising

counterintuitive or is it generic and

could apply to anything? And then next

always pull exact quotes from the

content to support your points. So this

is how it's evaluating. It's assessing

the voice looking for specifics. It's

checking for a payoff. Um, yeah. I think

the thing that's the most surprising to

me about this is how simple this

actually is. Um, like if I was to with

Claude write a prompt that did this, it

would probably, you know, this is 112

lines. It probably would have been many

more lines of like text and yeah, it's

very very interesting.

>> Well, let's test it. Let's do the

newlywood games with Dispan and Dan. Um,

and we'll we'll do like a whole uh you

know, we'll do a little like graphic

here and uh I'll I'll give a thumbs up

or down and we'll see what Dispy Dan

says and we'll see if we match.

>> Okay. I'm going to I'm going to find a

tweet on my timeline and send it to you.

>> Um I'm going to send it to you and then

I'm going to paste it into Disby Dan and

and what I want from you, Dan, is a

thumbs up or a thumbs down. Um

>> cool. Do you want any of my like actual

feedback?

>> Yeah, that that could be good, too. Um,

>> okay. Does it And it does it give it

gives feedback, right? Like it gives

Yeah. Yeah. So, we should we should

match the the thumbs up and thumbs down

be the main thing, but we should I'm

also curious about my my feedback.

>> Um, okay. So, the the the tweet you gave

me and we'll put it up so people can see

it. The tweet you gave me was, "I feel

like the VC community is doing a

disservice to the startup community

ecosystem by constantly organizing all

these events, especially on weekdays at

6 p.m. And I never understand how how so

many founders are able to make it to

these leaving office at 5:30 p.m. makes

sense from their POV."

Um,

this is very thumbs down. Sorry, Flo.

Um,

okay. So, first thing is

um starting a tweet with I feel like

um sometimes it it actually really does

work because it is

um

it feels more casual and like something

that you didn't edit, but also it it

lowers the impact of it. And so if

you're using I feel like you want to be

pretty intentional about that. Um

and because this tweet is

very counter it he's kind of going after

people a little bit. I think you got to

do it would be better with a full send

of

you know

the this the VC community is doing way

too many events like organizing so many

events. Um

I think uh oh you know what I was only

seeing part part of the tweet. Um okay

because I was looking at it in the

Discord preview. So it it continues. So

I feel like the VC community is doing a

disservice of this blah blah blah. I

never understood how many so many

founders are able to make it to these

leaving the office at 5:30 p.m.

Makes sense from their POV. They're

building a network and effectively

running the equivalent of an enterprise

sales motion. in the in the end it

results in dragging the community. Juu

founders dare to say no to 99% of the

invites they receive which hurts their

business. Um

the the thing I like about it is there's

something true here. Um

and it feels very uh stream of

consciousness like he just kind of was

in the car on the way to something and

was like blah blah blah here like here's

what I think. Um, and I think that

that's where all the best tweets come

from. And there's something interesting

about the core idea of it, which is um,

if I had to take a little bit further,

um, there are certain um, stable

equilibriums in ecosystems where um,

everybody in the ecosystem doesn't like

it, but you end up there. You end up

stuck there. So like having so many

events is um uh it's one of those things

where it's actually not good for anyone

because VCs are like I guess I have to

put on all these events and I'm wasting

money on it and people I guess show up

but I don't know if it's really that

differentiated and founders are like I

wish I could work. Um uh but uh but

everyone keeps doing it anyway because

they feel like they have to. So I think

there that there's a core idea there

that's kind of interesting. Um,

but I think this would just hit way

harder if it was um

I think VCs are actively hurting the

startup community by holding so many

events. Um, with maybe like one or two

more sentences of why that is.

um as it is it's like it's a little

wordy and he's also going after

um VCs and founders at the same time and

it's I think a little bit

it feels like um

you don't necessarily want to open up a

two front war at the same time. Um,

uh, so I I give him points for an honest

observation

that has there's an interesting idea at

the core of it, but I think execution on

this, it's not done in a way that allows

allows me to be like to light up and be

like, yeah, you're totally right. I'm

kind of like, I don't know, it's too

long to read. And I think he's kind of

making fun of me, too. Um, instead of

like getting me on board with him

because I would be down to bash VCs, you

know. Um but uh yeah that's that was my

feedback. Thumbs thumbs down.

>> Thumbs down. Do you because you you said

that thumbs down with such vigor um when

you first has it has it is it is a

softer thumbs down or is it still a is

it still a really strong thumbs down?

>> Um

>> that's me leading a little bit. It's a

I think it I think it is softer in the

sense that I only read the preview and

so it looked like it was it would just

cut off like makes sense from the P of

view was where it stopped and I was like

what even is that? So I'm a little less

um less vigorous, but it's still not not

a great tweet.

>> Okay, so this is what Dispan had to say.

So he uh gave the tweet a thumbs up.

>> Whoa. Um but what was interesting was

that actually you know you said similar

things slightly wordy in places um a

clear playoff which is like you know the

sorry yeah like clearly it felt like

he'd written this himself it felt

handwritten not AI generated but disban

felt like it was counterintuitive uh the

VC community is doing a disservice to

the startup ecosystem sort of like a

bold contrarian take um not something so

there is

>> I think there's clearly still work to do

I think disb Dan is really thinking more

about like is more what is this saying

as opposed to how is it written. So like

that is something we have other judges

that look at like how are things

written. Um so I think one of the things

that we found kind of said this earlier

having a model do many things doesn't

work having it do one thing is good but

sometimes that one thing isn't the whole

thing. Um, yeah. So, it' be interesting

to see adding this into some of the

other judges we have, how it would how

it act. But also, I think this is going

to be one of the things that I've come

to accept is that a product like this is

going to be always evolving. The um the

models are going to change. You know,

Sonic 4.6 is going to come out next week

or something like that. Um, and

>> I'm so tired.

And I think you know GPT5 was really

interesting of

to use it you have to prompt

differently. Um there was a while where

GPT5 was the writing model inside Spiral

and the prompt was so different. It had

all these XML tags and it was like very

and now we've gone back to a a clawed

model and it's a much different prompt.

Um, and yeah, I think it's

there is no I mean I don't ever really

think there was ever a thing of like

done when it comes to building products,

especially software. Um, I think it's

more so evolving because the product

itself is non-deterministic. You can put

whatever you want in the chat input and

whatever else it wants is going to come

out. And we want that to be outputs that

we can it's it's actually a really

interesting problem because you want it

to be outputs you you know if I was

making Instagram filters and if I was an

engineer at Instagram um I never was

just to be clear but if if that's what I

was doing and I was coding up a filter

um I could be like great this filter is

sick and it works for the things that it

and like I never have to touch it ever

again unless it breaks right but like I

don't have to whereas now I'm like I

actually can't I would love to say that

I could stand by all of the writing that

comes out of Spiral, but I can't. I

literally cannot do that because it

would require an infinite number of me

looking at an infinite number of

outputs, which is impossible. So, it's a

it's a really interesting like shift in

building products that um I don't even

think I've really fully come to terms

with just yet. Um

>> I think you're right like good writing

is alive. like it comes from something

that is alive and is honest to a living

experience. And so anything that is

static, it can be good for a while, but

over time it it becomes slop if it's

static. And that's something that I

think we're starting to grapple with and

um has some really interesting

implications for the direction of this

product over time.

>> Yeah. Yeah. And

I think I think it come has implications

for like what the next generation of

models look like and like what they're

trained on. And you know like a lot of

these models you know AISMs I I really

think the AISMs come from two things

which is one I think we just get used to

them. Um especially I think like us

because we're looking at AISMs all the

time. Um but then also just like the the

amount of nonsense that was written on

the internet over the last 15 20 years

and just how much of that is in the

training data. Um you know in an ideal

world anthropic would hire us to like

prune the nonsense out of their training

data and then train like sonet 4.9 um

with with every bespoke um training

data. That would be great. Um, Dario,

>> let's Yeah, if you're listening,

>> let us know.

>> Um, Danny, this is fantastic. I actually

feel like I learned a lot. Um, uh, like

I said, uh, so proud of you and what you

built and so excited for, uh, getting

this out into the world, getting into

people's hands, and and and also for

what comes next. Um, where can people

find you and where can they find Spiral

uh, if they want to give it a shot? So,

Spiral, you can go to writing.new is a

domain. It's also writewithspiral.com.

Um, it's trypiral on Twitter. Uh, you

can also follow every everywhere. Uh,

and you will find links for it. And I am

Danny Aziz 97 everywhere on the

internet.

>> Awesome.

>> Thank you very much for having me.

>> Thanks for coming on.

>> Of course.

>> Oh my gosh, folks. You absolutely

positively have to smash that like

button and subscribe to AI and I. Why?

Because this show is the epitome of

awesomeness. It's like finding a

treasure chest in your backyard, but

instead of gold, it's filled with pure

unadulterated knowledge bombs about chat

GPT. Every episode is a roller coaster

of emotions, insights, and laughter that

will leave you on the edge of your seat,

craving for more. It's not just a show.

It's a journey into the future with Dan

Shipper as the captain of the spaceship.

So, do yourself a favor, hit like, smash

subscribe, and strap in for the ride of

your life. And now, without any further

ado, let me just say, Dan, I'm

absolutely hopelessly in love with you.