OpenAI o1 VS Sonnet 3.5 in Coding Physics Games - AI Showdown

Summary

In diesem Video vergleicht der Sprecher zwei künstliche Intelligenz-Modelle, das Sonet 35 und das neue OpenAI Model, indem er sie dazu herausfordert, einen Auto-Parkplatz-Simulator mit Physik zu entwickeln. Während das Sonet 35 bei früheren Versuchen scheiterte, gelingt es dem OpenAI Modell in einem einzigen Versuch, was seine überlegene Leistung zeigt. Der Sprecher demonstriert, wie das OpenAI Modell auch komplexere Aufgaben wie die Entwicklung eines 3D-Parkplatz-Simulators annimmt, wobei es einige Herausforderungen und Fehler macht, die auf seine Grenzen hindeuten.

Takeaways

• 😀 Der Videoinhalt dreht sich um die Gegenüberstellung zweier KI-Modelle, um deren Leistung bei der Entwicklung eines Parkplatzsimulators mit Physik zu testen.
• 🤖 Das Modell Sonet 35 aus Anthropic wird mit dem neuen OpenAI-Modell O1 verglichen, wobei O1 in zwei Versionen existiert: O1 Preview (größer, langsamer, teurer, besser) und O1 Mini (schneller, kleiner, günstiger).
• 🚗 Die Herausforderung besteht darin, ein Spiel im GTA-Stil mit realistischen Physik- und Radverhalten zu entwickeln, was Sonet 35 bisher nicht schaffte.
• 📊 OpenAI-Modelle zeigen eine signifikante Leistungssteigerung in mathematischen Problemlösungen im Vergleich zu früheren Modellen, wie GP4.
• 💡 O1-Modelle sind speziell darauf trainiert, länger zu denken und ihre Überlegungen vor dem Lösen von Problemen zu verbergen, um dann eine Zusammenfassung zu geben.
• 🔧 O1 Preview schaffte es, den Parkplatzsimulator in einem einzigen Versuch zu entwickeln, was Sonet 35 nicht konnte.
• 🎯 Durch die Iteration mit dem Code von O1 Preview konnte WebSim das Spiel weiter verbessern, indem es zusätzliche Funktionen wie Parkplätze, eine Geschwindigkeitsanzeige und eine Punktzahl hinzufügen konnte.
• 🛠 Die Anforderungen für die KI-Modelle sind hoch, da sie nicht nur Code schreiben, sondern auch verstehen müssen, wie externe Bibliotheken und Physik-Engines funktionieren.
• 🔄 Es zeigt sich, dass selbst hoch entwickelte KI-Modelle wie O1 Preview nicht alle Aufgaben perfekt lösen können und es zu Iterationen und Fehlern kommt.
• 🔮 Die Zukunft der KI-Entwicklung scheint darauf ausgerichtet, intelligentere Modelle für schwierige Probleme zu nutzen und dann effizientere Modelle für weitere Verfeinerungen einzusetzen.

Q & A

Welche beiden Modelle werden im Video verglichen?

Im Video werden das Sonet 35 und das neue OpenAI Model verglichen.

Was ist das Hauptziel des Vergleichs zwischen den Modellen?

Das Hauptziel ist zu sehen, wie gut beide Modelle einen Parkplatzsimulator mit Physik schreiben können.

Warum wurde das Sonet 35 Model vorher nicht erfolgreich bei der Entwicklung eines Parkplatzsimulators?

Das Sonet 35 Model scheiterte wiederholt, weil es die komplexen Anforderungen des Simulators, wie realistische Physik und Raddrehungen, nicht korrekt implementieren konnte.

Was ist ein Beispiel für die verbesserte Leistung des OpenAI Model 01?

Das OpenAI Model 01 konnte 83% der mathematischen Probleme korrekt lösen, im Gegensatz zum vorherigen Modell, das nur 13% richtig beantworten konnte.

Wie unterscheidet sich das OpenAI Model 01 von früheren Modellen?

Das OpenAI Model 01 wurde speziell trainiert, um länger zu denken, uncensored, und dann seine Überlegungen zu summarieren und sie dem Benutzer zu zeigen, bevor es die Probleme löst.

Welche Einschränkungen gibt es bei der Verwendung des OpenAI Model 01?

Es gibt eine begrenzte Anzahl an Aufrufen pro Woche, nämlich 30 für das größere Modell (01 preview) und 50 für das kleinere Modell (01 mini).

Was versucht der Uploader mit dem OpenAI Model 01 zu erreichen, das er mit Sonet 35 nicht konnte?

Der Uploader versucht, mit dem OpenAI Model 01 einen Parkplatzsimulator zu entwickeln, der realistische Physik und Raddrehungen umsetzt, was er mit Sonet 35 nicht schaffen konnte.

Wie wurde die Leistung des OpenAI Model 01 in der Entwicklung des Parkplatzsimulators bewertet?

Das OpenAI Model 01 schaffte es, einen funktionierenden Parkplatzsimulator in einem einzigen Versuch zu entwickeln, was eindrucksvoll ist, da es ohne Fehler und ohne Korrekturen funktionierte.

Was zeigte der Uploader, indem er das Ergebnis des OpenAI Model 01 an das WebSim Model weitergab?

Der Uploader zeigte, dass das Ergebnis eines intelligenteren Modells (OpenAI Model 01) von einem weniger leistungsfähigen Modell (WebSim) korrekt verwendet und verbessert werden kann.

Was versucht der Uploader als nächstes, um die Grenzen des OpenAI Model 01 zu testen?

Der Uploader versucht, das OpenAI Model 01 zu einem noch schwierigeren Test herauszufordern, indem er es auffordert, einen 3D-Parkplatzsimulator mit realistischer Physik zu entwickeln.

Outlines

00:00

🚀 Vergleich von AI-Modellen für ein Parksimulationsspiel

Dieser Absatz stellt einen Vergleich zwischen dem AI-Modell Sonet 3.5 und einem neuen OpenAI-Modell vor, um zu sehen, wie gut sie ein Parksimulationsspiel mit Physik entwickeln können. Der Sprecher beschreibt, wie er bereits mit Sonet 3.5 experimentiert hat, das jedoch wiederholt scheiterte. Das OpenAI-Modell wird als ein reasoning Model vorgestellt, das in zwei Versionen verfügbar ist: '01 preview', das größer und langsamer ist, und '01 mini', das schneller und kostengünstiger ist. Die Tests zeigen, dass das OpenAI-Modell in der Lage ist, das Spiel von Anfang an korrekt zu entwickeln, während Sonet 3.5 immer wieder scheitert.

05:01

🔍 Herausforderungen bei der Entwicklung eines Parksimulationsspiels

In diesem Absatz wird erläutert, warum die Entwicklung eines Parksimulationsspiels mit Physik so schwierig ist. Es wird darauf hingewiesen, dass selbst kleine Fehler in der Physikimplementierung das Spiel unbrauchbar machen können. Der Sprecher beschreibt, wie er versucht hat, das Modell dazu zu bringen, ein solches Spiel zu entwickeln, und wie dies mit Sonet 3.5 nicht funktionierte. Es wird auch erwähnt, dass das OpenAI-Modell in der Lage war, ein Spiel zu entwickeln, das den Anforderungen entspricht, aber noch nicht perfekt ist.

10:02

🚗 Verbesserung des Spiels mit weiterführenden Anpassungen

Der Sprecher zeigt, wie er das von OpenAI entwickelte Spiel weiter verbessern lässt, indem er das weniger leistungsfähige Modell WebSim dazu verwendet, das Spiel zu erweitern und zu verbessern. Es werden Features wie ein Tachometer, Parkplatzzeichen, eine Punktzahl und Gebäude hinzugefügt. Es wird gezeigt, wie durch die Iterationen und Anpassungen das Spiel immer besser wird, und wie die verschiedenen Modelle zusammenarbeiten können, um ein besseres Endprodukt zu erstellen.

15:03

🛠 Test und Iteration mit dem GP4-Modell

In diesem letzten Absatz wird eine Live-Test-Situation mit dem GP4-Modell beschrieben. Der Sprecher will ein 3D-Parksimulationsspiel entwickeln und stellt hohe Anforderungen an die Realismus und Interaktivität des Spiels. Es wird gezeigt, wie das Modell versucht, die Anforderungen zu erfüllen, aber auch, wie es bei der Umsetzung in die Praxis Schwierigkeiten hat. Es wird betont, dass, obwohl das Modell schneller und fähig ist, komplexere Aufgaben zu bewältigen, es immer noch nicht auf der Stufe eines erfahrenen menschlichen Entwicklers ist und weiterhin an einigen Herausforderungen scheitert.

Keywords

💡Sonet 35

Sonet 35 ist ein Modell von Anthropic, das in der künstlichen Intelligenz für die Entwicklung von Programmen und Anwendungen eingesetzt wird. Im Video wird es mit dem neuen OpenAI-Modell verglichen, um zu sehen, wie gut es in der Entwicklung eines Parkplatz-Simulators mit Physik ist. Es scheitert jedoch wiederholt, was die Herausforderungen bei der Entwicklung solcher Anwendungen verdeutlicht.

💡OpenAI-Modell

Das OpenAI-Modell, speziell das AI1-Modell, wird im Video als eine der beiden Modelle vorgestellt, die in einem Vergleichsexperiment verwendet werden. Es besteht aus zwei Versionen: '01 preview', das größer, langsamer und teurer ist, und '01 mini', das schneller, kleiner und kostengünstiger ist. Das Modell wird für seine Fähigkeit gelobt, komplexere Probleme zu lösen, wie im Beispiel der mathematischen Probleme aus der Internationalen Mathematik-Olympiade.

💡Parkplatz-Simulator

Ein Parkplatz-Simulator ist ein virtuelles oder computerbasiertes Trainingswerkzeug, das verwendet wird, um das Parken von Fahrzeugen zu simulieren. Im Video wird der Simulator als Herausforderung für die künstliche Intelligenz modelliert, um zu zeigen, wie gut die verschiedenen Modelle in der Programmierung von physikalisch realistischem Verhalten sind.

💡Physik-Engine

Ein Physik-Engine ist ein Software-Tool, das verwendet wird, um die Gesetze der Physik in Videospielen oder Simulationen zu simulieren. Im Video wird es erwähnt, dass der Simulator realistische Physik haben sollte, was auf die Komplexität der Aufgabe hinweist, die die künstliche Intelligenz bewältigen muss, um das richtige Verhalten der Fahrzeuge zu programmieren.

💡Rechtschreibprüfung

Die Rechtschreibprüfung ist ein Prozess, bei dem die Schreibweise von Wörtern überprüft wird, um sicherzustellen, dass sie korrekt sind. Im Kontext des Videos wird dies als Teil der Herausforderung angesehen, die von der künstlichen Intelligenz bei der Codegenerierung bewältigt werden muss, um sicherzustellen, dass die Syntax und die verwendeten Befehle korrekt sind.

💡Code-Generierung

Die Code-Generierung ist der Prozess, bei dem eine künstliche Intelligenz oder ein Programm Code schreibt, um eine bestimmte Aufgabe auszuführen. Im Video wird dies als zentrale Fähigkeit der künstlichen Intelligenz modelliert, die in der Lage sein muss, den Parkplatz-Simulator korrekt zu programmieren.

💡Canvas

Canvas ist ein HTML-Element, das verwendet wird, um Grafiken und Animationen zu erstellen. Im Video wird es erwähnt, dass es für die Entwicklung des Parkplatz-Simulators verwendet wird, um die Darstellung des Fahrzeugs und seiner Bewegungen auf dem Bildschirm zu ermöglichen.

💡JavaScript

JavaScript ist eine Programmiersprache, die häufig für die Entwicklung interaktiver Funktionen in Websites verwendet wird. Im Video wird JavaScript als eine der Technologien genannt, die für die Entwicklung des Parkplatz-Simulators verwendet wird, um die Logik und die Interaktionen des Spiels zu programmieren.

💡CSS

CSS steht für Cascading Style Sheets und wird verwendet, um das Erscheinungsbild von Websites zu gestalten. Im Video wird CSS erwähnt, um die visuellen Aspekte des Parkplatz-Simulators zu beschreiben, wie zum Beispiel die Darstellung der Fahrzeuge und der Umgebung.

💡Interaktivität

Interaktivität bezieht sich auf die Fähigkeit, mit einem System oder einer Anwendung zu kommunizieren und diese zu steuern. Im Video ist Interaktivität ein wichtiger Aspekt des Parkplatz-Simulators, da es dem Benutzer ermöglicht, das Fahrzeug zu steuern und die Auswirkungen seiner Steuerung auf das Parken zu sehen.

Highlights

Comparing Sonet 3.5 and OpenAI's new model for coding a car parking simulator with physics.

Sonet 3.5's repeated failures in creating a physics-based car parking simulator.

Introduction of OpenAI's new reasoning model, featuring two versions: '01 preview' and '01 mini'.

Limited access to the new models with 30 and 50 calls per week respectively.

The new model's ability to solve 83% of mathematical problems in the International Mathematics Olympiad, a 600% improvement over previous models.

The model's training to think longer and uncensored, then summarize its thinking for problem-solving.

Testing the new model by asking it to build an educational parking game with realistic physics.

CLA's initial failure to create the game, despite understanding the prompt.

Web's attempt with the same prompt, resulting in errors and eventual partial success.

The complexity of integrating physics engines for top-down games and the model's challenges in handling it.

OpenAI's '01 preview' model's successful creation of a basic, working car parking simulator in one attempt.

The impressive single-shot success of '01 preview' compared to Sonet 3.5's repeated failures.

Demonstration of using the output from '01 preview' as a starting point for further improvements with a less capable model.

The potential of using smarter models for complex tasks and cheaper, faster models for routine improvements.

Live testing with the GP4 model to push its limits by asking for a 3D parking game with physics.

The model's struggle with the complexity of creating a 3D game, showing it's not yet at human-level development.

The model's iterative process in attempting to correct the wheel positioning in the 3D game.

Final thoughts on the model's capabilities, its room for improvement, and future potential.

Transcripts

hello today in this video we're going to

pit two models one against the other to

see how they perform one model is Sonet

35 that you can use in Claude or in

websi another model is a new open ai1

model and the way we're going to do it

is by asking them to write a car parking

simulator with

physics and this is something that Sonet

35 repeatedly failed to do for me before

so it's going to be interesting why this

going to be interesting well o one is

new open AI reasoning model it's

actually two models in chpt you will see

01 preview which is larger slower more

expensive better model and there is 01

mini which is faster smaller cheaper you

will only get 30 calls to o1 preview per

week and 50 calls to One Mini per week

so kind of limited for now now what does

mean that it's a reasoning model well

one example Opia gives in their blog is

this number their previous best model

gp4 could only solve

13% of mathematical problems correctly

in international mathematics Olympiad

while 01 can score 83 This is 6X

Improvement not 100x Improvement it's

still 600% Improvement so not as high as

hype but pretty high and very very

impressive how does it do it well it was

specifically trained to think for longer

uncensored in a way that open a hides so

it thinks but you do not see it then it

summarizes and sensors its thinking

shows it to you and proceeds to solve

your problem and answer your questions

this together allowed it this kind of 6X

Improvement which is again very

impressive but I wanted to test it for

myself how can I myself build this

Improvement

and what is the easiest way to do this

to answer like is it better the easiest

way is by comparison so I want to

compare it today to a previous best

model I know of which is Sonet 35 from

anthropic in Cloe and webin now however

good the model is however impressive it

is it did very impressive things for me

before surprised me I asked it once to

write for me educational game of how to

park top down game with physics with

rotating Wheels with acceleration with

brakes

and with Trails for car wheels to show

where everything is when you drive kind

of a visualization for you to learn how

to park correctly and this is what I

asked before I asked it to ride this

kind of game and it was failing time and

time and time again well today we will

try and see what 01 can do for this

problem so this is a fight and this is

round one let's

go okay so here is a chat I had with CLA

I copy pasted my prompt it lost the

formatting I'm asking here for HTML CSS

and JavaScript game in GTA top down

style it should be parking game car

should have four wheels they should

rotate they should be realistic physics

and friction and it should be parking

game it leaves Trails uh behind it

Wheels like Trails of Wheels okay not

super prompt there just something I

brought

quickly here is what LO understood and

try to do so it said yes I will create

for you this kind of HTML CSS JavaScript

game with rotating Wheels realistic

physics and whe

trails and WR like everything else I can

see that it understood correctly what I

wanted and it wrote

this a gray Square nothing happens you

cannot write nothing happens no errors

nothing so it fails blot failed from one

shot we can take a look at code as far

as code goes it didn't use any kind of

third party libraries there is some CSS

and there is Javascript it uses canvas

it wrote All on its own it just not

working considering there were not even

errors it's even hard to say what's

wrong okay next let's see what happened

in web it uses the same model we can see

that I have here the same prompt just

formatted here with lines and from first

try also failed it showed an error and

when I've tried to fix the error it did

this exactly like I wanted

right uh I did ask it to change some

things and got

this now at least it doesn't fall down

partially problem is that I asked it for

physics game and i' used physics engine

made not for topown games but for Sid

scroller

games but we can also here see why this

prompt is problematic it includes using

physics engines a third party dependence

and physics engines are notoriously very

lacky in a sense that even small

mistakes in how physics Works makes them

go crazy that's what you see here so

asking model to write this kind of code

it needs to write a code it needs to

understand how to use library and it

needs to understand what I asked in

context that I want a top- down parking

game how do you use physics engine

correctly to make it work and also

render correctly this is a very it's not

easy CU we are asking a textual model it

never seen things how can it reason

about physics and how to use physics

engine for it to correctly simulate what

what I'm asking this is this is hard

even with iteration with son 3 in vbim

or in clo this thing fails for me I've

tried multiple and multiple times before

I wanted to make this to help my wife

illustrate how parking works when and

how you should reason about where and

where your car is and how different

parts trajectories go when you're

parking and I couldn't do it and I don't

don't have time to sit down and do it on

my own it could take takes it's not

actually easy to do this from scratch to

work correctly so Sonet and Claud and

web fail for me with this kind of PR

let's take a look at what openio wanted

here is the same PR here is chat with uh

ch1 preview the slower more expensive

smarter model it thought for 24 seconds

it didn't show whole reasoning it just

showed summary of it and we here see

that it says crafting the simulation I'm

creating GTA 2 style top down car game

it will be HTML and

JavaScript it will Design on

canvas will be

controls mapping out the game I'm

setting up HTML the game Conners will

serve as rendering area setting up

setting up environment I'm pulling

together the game conas driving car

class focusing on speed acceleration

steering position updates and rendering

we can see that it's reasoning through

in different ways through what it's

going to be doing then it would have

enchancing car physics I'm working

through realistic car physics including

wheel rotation it's like selft talk you

know like it's self-affirmation what

humans do in the sense that what is it

that I'm doing now oh yeah that's what

I'm doing okay let's do

it so interesting what's interesting

that clae also does that we can see here

some of its reasoning being put out but

there is also hidden part of the

reasoning that they do not show we know

from proms that they have thinking parts

and they are not showing them here

they're hiding them so CLA does this too

and this does this too does it perform

better well it generated this kind of

code actually big one usually chipt was

not generating such a big one code from

what I seen o1 preview and o1 mini can

output very large answers something like

eight or more like 8 to 16 times larger

than before this is not 8 to 16 times

larger than before but it's an large

answer and we can say that it gives us

explanation like there is HTML convas is

of this size there are colors there is

car code it explains everything about

the code you can read it and learn on

some level of what the code it wrote

does

it has features realistic physics how to

use use with this arrows and we have

code what we can do is we can copy that

code we can go to code pen past that

code let's save so that we have empty

one save again and this is what we

got it's a little bit

unfinished but it's

working Wheels rotate going I can go

back I can go forward and it is drawing

a line only issue is that this line

seems to be the middle of the rectangle

of the car it's a little bit not what I

want I probably would draw actually

Trails of corners of the car's body I

think this is the most important thing

for this kind of game but um it did it

it did it from first try single prompt

one shot no correction it just

works this is impressive

so that's it I just compared Sonet to 01

model 01 did in one shot something I was

trying to achieve with web seam or

CLA it's not

perfect then one more interesting thing

happened I've gave this code to web seam

to see what it's going to do let's take

a look at

that so I think it was here here I gave

web Sim the same code and it worked we

have the same car that 01 generated but

webam added other things we can see

speedometer we can see parking spots we

can see score we can see buildings and

if we go into the building it's going to

become

red we can go and try to

park oh we got 100

score and four parking spots remaining

we can go to the next

one I also wanted a little bit more work

around the

trails now we have four

trails and we have also some

settings so we can change acceleration

and some other things now it's right

faster we we we we we we oh my

God so what happened here and we can go

and see the prompts so we can see that

first prompt was just code that o1

generated and then I said that make it

draw four Trails one for each will leave

other things as this add settings

control minimal and maximal speed

friction control and so on make left

right arrows rotate the wheel and so on

so on so I I iterated little bit and I

got even better version so what's

happening here is is I've gave result of

smarter model presumably seems like o1

is smarter than son 35 I gave thinking

from smarter model to a less capable

model and it was capable of using it

correctly not breaking it and improving

Aon it which is interesting and

impressive use case you can use smarter

models to get you started or for hard

problems and for other things use

cheaper faster models it's an

interesting future we're going into so

this is another thing I wanted to show

now all of what I showed you I've did

yesterday evening now I want to do a

little bit of live testing with you the

gp4 model and here I want to create so

we using preview I want a new

chat so what I'm thinking I still

slightly dislike this I think it's not

realistic enough in some

ways it doesn't really behave like a

physical

car and it got me thinking while o1 is

impressive it moves the button further

it can do more it's not that sky is the

limit ceiling has risen but like but

it's lower than the sky and this is what

I want to show you because I was playing

with it a little bit and it does fail it

just it does more complex things and

fails less it still

fails so what I was thinking let's ask

it to make a 3D game let's make it even

harder let's try to push it Until It

Breaks A little bit let's make a harder

game I want

HTML JavaScript and

CSS okay it should be in 3D with physics

it should

be parking

game there is a car four wheels front

wheels

State wheels are round there is friction

inertia and everything else to make it

realistic purpose of the game is to

allow people to learn how to park how to

think and reason about when and why and

how you need to rotate the whe

will what are

trails and trajectories of wheels and

car

Corners so people can learn in the game

and apply that knowledge to real

parking let's ask it this what's

interesting here and I I do suspect it's

going to fail what's interesting here is

that I'm now asking it to use third

party Library game engine and it's in 3D

and it should be realistic and there is

purpose in teaching people how to park

let's see what it's going to

do it's very challenging Asar in some

ways so it's thinking it's

crafting so crafting parking game 3D in

HTML JavaScript CSS setting up 3D

elements crafting a game creating a

realistic 3D parking HTML game it will

use 3GS

library and Canon G for physics note due

to the complexity of full-fledged game

the following code provides a basic

framework you can expand upon it to add

more features

okay

okay so what I dislike so far is that it

writes it as a bunch of different

segments I want to ask it can you write

it as one large HTML code block I can

copy and paste at

once so on the reasoning side I think

it's good choice three jazz is most

popular Library Canon Jazz I do not know

I have not much experience with 3D

physics but it seems more or less

correct it creates the car there is

geometry there are wheels and there is a

lot of opportunity for failure it is

very hard task so now I asked it it to

do a one block it thought for 7 seconds

and wrote for me a block that actually

looks

smaller is it we can see it's probably

not as good let's create a new fan see

what this small block

did okay it's

actually the only thing it's not

writing can

I but it's almost

there um

okay I wonder if we could add all of

that let's try to

check uh

if we search for this it is here twice

okay okay let's give it feedback let's

see if it can

iterate oh I I see that wheels are in

different directions it cannot analyze

images as well so it's not multimodel

model just yet I didn't check in maybe

it is multimodel Data just didn't expose

the capability oh what's interesting it

hand it still continue to

work it didn't finish uh so let's give

it feedback it seems like wheels are not

correctly

positioned they

go parallel to the

ground ground so when I press arrows car

does uh right anywhere anywhere can you

ruminate

and why and how to

fix so I don't want to make this video

too long after this iteration we're

going to stop for this last part I just

wanted to demonstrate that it's not

magic it is better in some ways it's

considerably better but it's not human

level developer yet on other hand it's

fast it is faster than human developer

doing these kind of things so it's again

noticing issue the user has been point

of the wheels is parel to the ground

causing the car to stay motionless when

arrows are

pressed hang out the options analyzing

code

setup ensuring correct wheel setup

navigating orientation

differences oh it's thinking it was

taking for a while and it failed so yeah

no magic there let's

see okay this time it thought slightly

less

and it's writing new code well let's

keep some fingers crossed maybe it's

going to

work as you can see it writes more code

I wonder how many lines that is I

remember they were speaking about

something like 32,000 16,000 64,000

tokens this is a lot it wrote A big one

it explains what it try to

fix it even wants to run oh boy this is

going to be interesting in ating

workflows it speaks about how to test

that you will not run to server open it

I wonder if it's going to be able to

manipulate a browser

eventually okay so we here have no new

code let's give it a try

sa and I think it completely failed and

it failed so not magic it's better and

impressive but still will require

iteration there will be type of problems

for which it's going to be failing this

is all I wanted to show today it's

better it's going to be interesting to

see where it goes this was round one in

it it did win against son 35 I will be

playing with it more during next weeks

and I will make another video uh if I

learn anything interesting so if you

like this kind of things push subscribe

down there and see you next time