Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Animator with limited coding experience builds a fluid movement system using Unity and AI-generated code. 🏃‍♂️ animator JankyAnims shared how they used ChatGPT to piece together "spaghetti AI code" to get their wall-running and acrobatic movement mechanics fully playable in Unity.

39,888 görüntüleme • 18 gün önce •via X (Twitter)

10 Yorum

desfrect profil fotoğrafı
desfrect18 gün önce

“Hey ChatGPT, I want you to de-spaghettify this code”

tofuborne profil fotoğrafı
tofuborne17 gün önce

manual devs:

Robert Varadan profil fotoğrafı
Robert Varadan18 gün önce

You are going to see a lot more of this sort of thing!

godraytio profil fotoğrafı
godraytio18 gün önce

Now make a game cool demos are everywhere. Dont sohw anyone anything till you near done.

COBOX profil fotoğrafı
COBOX18 gün önce

AI + game dev 👀

Gaz profil fotoğrafı
Gaz17 gün önce

So good!

OneOverR2 profil fotoğrafı
OneOverR218 gün önce

Playing this game is very relaxing.

WhunoYou profil fotoğrafı
WhunoYou17 gün önce

Basically took sonics code

wolfengine.co profil fotoğrafı
wolfengine.co18 gün önce

AI + game dev = Unlimited creative games. Cant wait for the future

CJ シージ profil fotoğrafı
CJ シージ18 gün önce

They use Ai ?

Benzer Videolar

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 görüntüleme • 1 yıl önce