DiceBear is a free, open source avatar library and avatar API with 61 styles. Create deterministic SVG profile pictures and placeholder avatars for user profiles, apps, and any project. Privacy-focused and self-hostable.
Your Free Avatar, Profile, and Link In Bio | Gravatar
Create a global avatar and profile image with Gravatar. Get a free one-page personal site to showcase your identity and connect across the web.
How I made an automated dynamic avatar for my Bluesky profile - dame.is
Several years ago, back when Twitter was a thing and it had a decent API, my friend @goose.art did a weird little experiment where he made his avatar dynamic... it automatically changed appearances on a regular basis and became increasingly deep-fried (increased in distortion and lossy compression). I've seen several other people mess around with this concept since then, and I've always had it in the back of my head as something I wanted to eventually try for myself.
hailey.at/skyembed
A lil service that creates embeddings of posts, profiles, and avatars to store them in Qdrant
How I made an automated dynamic avatar for my Bluesky profile — dame.is
Several years ago, back when Twitter was a thing and it had a decent API, my friend @goose.art did a weird little experiment where he made his avatar dynamic... it automatically changed appearances on a regular basis and became increasingly deep-fried (increased in distortion and lossy compression). I've seen several other people mess around with this concept since then, and I've always had it in the back of my head as something I wanted to eventually try for myself.
Download PNG & SVG Icons: Your Ultimate Icon Library
Explore our free icon library with unique PNG & SVG sets, crafted from scratch by 8 rebel designers. Download innovative and harmonious icons now.
I Cloned Myself With Gemini’s AI Avatar Tool. The Result Was Unnervingly Me
I used the Gemini app to generate lifelike videos featuring a digital clone of myself. Google sees this as the future of creation. I’m still creeped out.
ewanc26/bluesky-avatar-updater
Contribute to ewanc26/bluesky-avatar-updater development by creating an account on GitHub.
BytePlus Free Trial: 4K AI Image/Video Models + LLM Tokens | 200 Free Images
BytePlus free trial, Seedream 4.0, Seedance 1.0, DeepSeek V3.1, GPT-OSS-120B, free 4K AI images, AI video generation, BytePlus LLM tokens
Skycle.app — Generate your friends Bluesky circle easily
Generate your circle with avatars of your friends Bluesky easily
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
Vaibhav (VB) Srivastav on Twitter / X
Hello local LLM enjoyers, starting today you should be able to find if you can run a GGUF directly from the Hugging Face page! 🔥Super psyched to see this out in the wild - this has been a big ask from the community!P.S. It'll pick all the hardwares you've defined under your… pic.twitter.com/7PTFCUHftl— Vaibhav (VB) Srivastav (@reach_vb) April 1, 2025
