Deep Seek just dropped Deep Seek harness

and it already has 190,000

stars. People are calling it the free

Claude code. So, in this video, I'm

going to install it, show you how to set

it up, and then show you exactly how to

run it. By the end of this video, you

will have a coding agent that is yours,

that no company can take away from you,

and that you can point at literally any

model you want. Let's get started. The

model is the brain. Everything you wrap

around that brain is the harness. Your

contacts, your tools, your plugins, your

MCP servers, your permissions, and your

memories. Claude code is not the

harness. Claude code is a harness where

somebody else made every decision inside

it and then closed those decisions. Deep

Seek harness ships the same job with

every one of those decisions still open

while it is running. Here's what that

means in practice. Pull a component out

of a normal coding agent and it breaks.

Lose a dependency halfway through a task

and it breaks unless you hard coded a

plan B in advance. In this one, every

component declares what it needs and the

runtime keeps the undo button for you

available for every change it makes.

Nothing is hard coded, so nothing has to

restart. You're going to see that later

in this video. There is a formal paper

behind this and a framework called

Cordis, but I'm not going to read a

paper for you. But, here's why I'm

making this video. The part of an AI

agent that used to cost you money just

went to zero. Everything written about

this so far has been written about the

repository, not from using it. So, what

we're going to do is install this

together and give it a real job. In In

install it, we're going to open the

website, and we can see it right here,

right at the front. Everything is a

plugin, and you're going to understand

why they say that in just a bit. And we

also have the GitHub repo over here.

190,000

stars. That is insane. The fastest

growing repo on GitHub by far. And we

can either get these from the website or

from the repo, but I've gone ahead and

copied this, and I'm going to run it on

my terminal right now. I'm just going to

paste it, hit enter, and let it install

itself. We'll give it a few minutes.

It's going to take about 5 minutes, but

here's where we are. We have our

dashboard. It actually just opened it,

and just like that, this is how fast the

install is. When it finishes, it will

open this local web app dashboard. And

the first time it loads, it's going to

ask you for a Deep Seek API key, but you

do not need to paste that in. You can

click configure it later, and we can

actually put in another API or whichever

API model that you would like to plug in

here. I'm going to go to settings, to

models. I'm going to add a provider, and

I'm going to be using GLM for this or my

GLM subscription. Applying it over here,

and boom, it's green. It's life. We're

good to go. The great thing is that the

model is a plugin here. Everything is a

plugin here, which means you're not

stuck with Deep Seek's paid API just

because Deep Seek wrote the harness. I

mean, a great thing that you can do if

you want to actually run it for zero

cost, absolutely for free. So, if you

have any open local models, you can go

to add a custom provider, put in the

provider's ID, display name. Here, you

can name it whatever you want. Say, for

example, you're running it on Ollama,

paste the base URL along with your the

key, and click on create provider. I'm

going to use this with my GLM

subscription that I'm paying $10 a month

for, and that is more than enough for

this. But, feel free to input whatever

subscription or API key or local model

that you have. A quick disclaimer

though, free hosted models have caps.

For example, Google's free Gemini tier

also works here, but they stopped

publishing their limits. The only

genuinely uncapped free path is running

the weights on your own local machine.

If you have a decent graphics card,

Ollama has one of the best one-line

installs for exactly this, and there's

no meter at all. I do not have that on

this hardware, so I'm not going to show

you something I can't actually run. What

is free forever, regardless of your

situation, is the harness itself. The

model is the only part anybody can ever

charge you for, and you get to pick who

that is. But, the way to find out what a

coding agent is actually worth is to

give it something real to do. Everything

is a plugin. That is literally the

headline on Deep Seek's own front page,

and they mean it far more literally than

you would expect. I mean, literally, you

can go to settings, go to plugins, and

you'll see that literally every single

thing in here is a plugin that you can

disable and enable. You can customize

this as much much as you want. Every

part of it is customizable. Literally,

the sidebar you're looking at right here

is customizable. The commands are a

plugin, and you can disable them. This

means you can add and create as many

plugins as you'd like into the harness.

Over here, we have creator mode that you

can select just like that, and that is a

preset that loads the plugin development

automatically. So, I'm going to build

the thing every coding agent should

already have, but none of them actually

do. And this is the prompt. Add a

plug-in that fires a Discord

notification when a run finishes and

puts the token count and what it cost me

into the notification itself. How crazy

is that? I mean, so many times I would

be just working on something else and I

would have another agent work on

something that runs for like 9 minutes

and I completely forget about it because

it's in another window and I never get

to see it finished its run. So, I'm

going to make the thing I wish I had

into a tool myself, like this. And while

it does its work, let me show you the

part that made me actually pay attention

to this thing. The job is running behind

me right now and I can watch every

single thing it's doing while it does

it. Every single run is traceable. The

context is traceable. The assistant, the

tool calls, every single call on this

list is traceable. Every single error

that you're seeing right now is

traceable. Not summarizable, traceable.

When you open the trajectory, you get

the exact system prompt that it used

right here. You get to see the context

that it injected word for word. You get

to see which skills loaded, which

didn't, and also why. Every tool call

with the payload it sent and the result

it got back, the thinking, how long each

step took. And you can even see the

payload, the result, the timing, how

long this took. Everything I just said

about components coming and going safely

sounds like gibberish until you test it.

So, I'm going to kill it mid-task. It

did complete the task we gave it, so I'm

going to give it another one so we can

kill it while it's doing it.

I'm going to ask it to make it into an

on-screen window in this session as

well. I'm going to hit run and then I'm

just going to let it start just a bit.

Let's look at its trajectory, what it's

doing. There you go. It's bringing in

the context. We can see turn two where

it started. And just like that, I'm

going to kill it right here.

Zoom where we left off, and I'm going to

send this right here. And you're going

to see that it's going to quite

literally pick up where we left off with

the same exact context that the same

exact user preference, the same exact

assistant and tool call that it listed

out. Because guess what? We have all of

this data. Now, to be fair to this, it

is a developer preview. The only tagged

release is a release candidate. So, what

I'm showing you right now is in its

beta. And the readme file literally says

in capital letters that there will be

breaking changes. Things will go wrong

for you that did not necessarily go

wrong for me, or things might go wrong

for me that will not go wrong for you.

But that is the difference. When this

breaks, it can come back. And the reason

it comes back is the same reason this

next thing I'm about to show you is

possible. So, let's go ahead and see

what it made. And there it is. A

[clears throat] notification bar just

right here. Now, obviously some things

to fix like the the black on black text

because we can't really read it unless

we highlight it. And it told us that we

need to load a job for us to test it in.

So, this would be the first job that it

does, the first run that it does while

being tracked. So, let's go ahead and do

that. Let's test it out. Also, please

change text color to white

so we're able

to read it. So, there you go. This run

just finished, and it fixed the color.

As you can see, the counter is broken.

So, I've gone ahead and told it to fix

it for us. But that is what a plug-in

looks like. Literally, you can create as

many plug-ins as possible as you would

like. You can disable them by clicking

this. If you click it, then there's also

a play button, so you can enable,

disable as much as you'd like, and you

can customize this as much as you want.

And this, ladies and gentlemen, is

something that you cannot get on Claude

Code or on Codex, either. Here's the

comparison between Claude Code and Deep

Seek Harness in one table. Yep, read

that last row twice. And then, let me

show you the thing that makes this whole

comparison collapse. There are two

packages sitting inside this repo that

almost nobody is talking about. One is

called {dash} sub agent Claude Code, the

other is called {dash} sub agent Codex.

These are not model providers. This is

not an Anthropic key in a settings box.

They spawn Claude Code and Codex's

actual child processes and hand them a

task. So, your agent gets two new tools,

one called sub agent Claude, one called

sub agent Codex. It can decide by itself

that a job is better handled by Claude

Code and then go and run Claude Code.

Now, there are three things you need to

know about this. It is one shot. Every

call starts a fresh process and a

conversation that cannot be resumed. One

task, one answer, no follow-up. Both

providers load dormant, so the preset

has to decide to give your agent the

tool. This is not switched on the first

time you open it by default. And the one

that will catch you out, it strips

credentials, shaped variables out of the

environment on purpose. So, whatever key

is already in your shell does not reach

the child. You have to pass it in

explicitly, which means this thing is

not a competitor to the agent you're

already paying for. It is a layer that

can drive it. This is how you can think

of this. Every take on this has been a

versus. This versus Claude Code, this

versus your current agent. Pick one.

That framing is wrong and the documents

themselves say so. Anthropic is in the

provider list that ships with Deep Seek

Harness. So is OpenAI, so is Bedrock,

Vertex, Codex. So you add a provider,

pick Anthropic, put your key in and now

you're running Opus inside the free

harness with the trace and with the

plugins, which means the question is not

Deep Seek or Claude. The harness and the

model were always two separate purchases

and one of them just went to zero. One

real limitation that is an API key, not

your Claude subscription and Claude API

is not famous for being cheap.

For two years the agent and the model

came as one product. If you wanted the

good model, you took whatever harness

came bolted onto it. That just stopped

being true. If I were starting today, I

would install it, point it at a free or

a cheap model for a week on the work

that does not need a frontier brain and

see how much of my month that actually

covers. Comment which model you're going

to point this at first because I want to

know whether everyone lands on the same

one. And if you have already run this,

tell me what you thought and what you're

currently using it for. If you enjoyed

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