Regardless, I think 2025 is going to be an interesting year for CTF...

It was surprisingly capable! Able to automatically recognize a function like the following as an AES S-box based key expansion, and write Python to solve it automatically.

Placed 2nd last weekend with SuperDiceCode at DEF CON Quals 2025! -- Here's a brief retrospective about using an LLM agent to solve (part of) the nfuncs challenge: c.mov/nfuncs-agent/

me when im ctfing

Finally, lets hypothesize a `Gif` object on which we can `add_frame`:
---------------------
g = Gif()
for i in range(10):
c = Canvas()
c.add_random_shapes(num=100)
r = c.render()
g.add_frame(r, ms=20)
g.save('./out.gif')
---------------------
Producing:

Now that we have this implementation however, we can adjust parameters without needing to invoke the LLM again:
---------------------
c = Canvas()
c.add_random_shapes(num=100)
c.draw()
---------------------

Let's hypothesize an API which places random shapes:
---------------------
c = Canvas()
c.add_random_shapes(num=5)
c.draw()
---------------------
LLM is invoked to figure out what `add_random_shapes` should do, and we get:

Now we introduce a new undefined api:
---------------------
...
t = Triangle(width=3, height=5)
t.set_origin(6,6)
t.set_color('blue')
...
c.add(t)
---------------------
LLM is invoked to update the context code and we get:

The existing context code can extrapolate to new usages:
---------------------
...
r2 = Rect(width=3, height=3)
r2.set_origin(2, 2)
r2.set_color('red')
r2.set_rotation(deg=10)
...
c.add(r2)
---------------------
We don't need to invoke the LLM here, but can render:

E.g. lets write the following:
---------------------
r = Rect(width=4, height=6)
r.set_origin(5, 4)
r.set_color('green')
r.set_rotation(deg=45)

c = Canvas()
c.add(r)
c.draw()
---------------------
LLM generates context code that allows us to render: