CREATING A ZOMBIE CHARACTER IN BLENDER FOR UNREAL ENGINE 5
Creating my low-poly zombie character in Blender was an exciting challenge, all while preparing it for use in Unreal Engine 5. I started by blocking out the basic shape with simple geometric forms, ensuring the model would maintain a clean, minimal polygon count suitable for real-time rendering. Through a combination of sculpting and vertex manipulation, I refined the character’s features, paying close attention to the exaggerated proportions that bring a cartoony yet eerie vibe to the zombie.
LEARNING UNREAL ENGINE 5 - PART 2
It has been a couple days since I’ve completed Bad Decisions Studio’s Unreal Engine 5 Beginner Tutorial. Hats off to them for the content, it really helped get me up to speed in a short time. It has even encouraged me to take the next step and begin prototyping a game idea I’ve hard for awhile. Hope you enjoyed following along on my progress at Imgur, and enjoy a the final video rendering
LEARNING UNREAL ENGINE 5
I’m still in the early stages of learning Unreal Engine, but have had a great time following the Bad Decisions Studio’s Unreal Engine 5 Beginner Tutorial. Like most of my projects I’ve decided to take checkpoint pictures to visualize the progress, and to keep me encouraged throughout. After two days of watching I’m pretty impressed with my progress, and even more impressed with Unreal Engine in general. Enjoy a short video clip of me panning around the scene:
EARLY EXPLORTATION ON LOCAL RAN STABLE-DIFFUSION MODELS
I’m in the early stages of exploring AI-powered image generation, one of the most exciting developments has been the ability to run Stable Diffusion models locally, even on average consumer-grade hardware like the NVIDIA GeForce RTX 3060 Ti. Despite not being the top-tier GPU, this graphics card still provides enough power to experiment with high-quality image refinement techniques. For this demonstration, I started with a low-quality reference image of a race-car (512x512 pixels).
CREATING STICKERS WITH AI ASSISTANCE
Recently I’ve seen loads of arts and crafts on my social platforms, and I wanted to see how accessible some of these online sticker printing services were. As someone without a talent for drawning I also wanted to see how far AI, most specifically DALL-E could take me. I’m no stranger to photography, and vector art softwares, and for this challenege I used Gimp and Inkscape as I’m running a pure Linux environment as my daily driver (and wanted to use open source software).
BUILD AI-GENERATED MICRO-SITES WITH LANDER!
Excited to unveil my latest passion project, Lander! Combining the power of AI with web site management, Lander is not just another CMS, its integrated cron scheduling feature empowers users to automate content creation seamlessly.
AI GENERATED BLOGS
Found myself with a bit of extra time on my hands lately, so I decided to take the opportunity to explore some AI generated content in the form of micro blogs. My goal was to write some python that interfaces with OpenAI’s api (gpt3.5 turbo model), writes focused content on randomized topics, and then warps that up in a Hugo static site, aka micro blog. I started with an expandable YaML configuration.
CAN NOSTR EVENTS BE MANIPULATED?
If you haven’t been following along, I have a couple of nostr posts at this point. https://nessy.info/tags/nostr/ I’ve been trying to further understand nostr by deep diving the protocol. At this point my thought is how mutable are nostr messages (events), I understand that during broadcast the relay verifies the signature, but then they need to store these events in some centralized database, right? Could a rogue relay for example accept your event, then alter it at a later time?
SIMPLE TOOLS FOR INTERACTING WITH THE NOSTR PROTOCOL
If you haven’t had a chance to ready my previous post about nostr, it is probably worth checking out as it give a detailed, step by step explanation. https://nessy.info/post/2023-02-16-deciphering-nostr-and-its-private-keys/ With knowledge gained from the previous post I decided to put together a couple of rough python scripts, this is to handle a few of our previously manual steps. Head on over to my Github and check out my nostr_stuff repository:
SIMPLE CLI FOR CATEGORIZING AND SENTIMENT OF TEXT
Now that I’ve spent some time with huggingface.co, specifically their NPL Course (natural language processing) I wanted to combine a couple of the learnings into a simple python script. What I ended up with was a script that could both categorize using a zero-shot-classification model, as well as get sentiment using a sentiment-analysis model. You can interact with this script in one of two ways, first by sending a string as input during execution: