ginigen-ai/smol-worldcup
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ginigen-ai/smol-worldcup updated
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Heartsync/NSFW-Uncensored-image2 updated
a Space 9 days ago
Heartsync/WAN-VIDEO-AUDIO Organizations
liked a dataset 6 days ago
updated a Space 8 days ago
updated 7 Spaces 9 days ago
WAN 2.1 FAST VIDEO with AUDIO
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33
Generate animated videos from images with optional audio
Wan2.2 14B Fast
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Wan_VO- generate a video from an image with a text prompt
WAN 2.1 fast T2V FusioniX
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Generate cinematic video from a single image and prompt
Hentai Adult
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125
Generate custom AI images from text prompts
PornHUB - Text to Image
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219
Generate NSFW images from text prompts
NSFW Uncensored - Text & Imagery
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240
NSFW Uncensored Text & Imagery for AI Limits
Final Bench
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a
liked a Space 13 days ago
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1
#1 opened 15 days ago
by
taejong
updated 5 Spaces 17 days ago
Uncensored i2v
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42
Free AI Creative Tools - www.humangen.ai
Qwen Image LoRA DLC - Multi Image
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2
Free AI Creative Tools - www.humangen.ai
NSFW Uncensored - Text & Imagery
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7
NSFW Uncensored Text & Imagery for AI Limits
NSFW Uncensored Adult Image
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13
Based 'Z-IMAGE TURBO'
FREE NSFW HUB
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671
Best & Free NSFW AI services
reacted to SeaWolf-AI's post with π 17 days ago
Post
2728
AI Is Training on Your Content Without Permission β Fight Back with Invisible Watermarks
FINAL-Bench/security-scan
Most generative AI training data is crawled without consent. Your text gets summarized, images reprocessed, videos clipped β with no way to prove you're the original creator. Existing watermarks are either visible or wiped out by a single AI preprocessing pass.
Detect Before, Track After
Pre-embed β Detect theft without any watermark. Text plagiarism check, image similarity analysis (perceptual hash, SSIM, color histogram, feature matching), and video temporal matching catch copies, edits, and excerpts.
Post-embed β Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence.
Text: 4 Independent Layers
Four mechanisms work simultaneously: zero-width Unicode characters at morpheme/word boundaries (Korean Kiwi + English NLP), style fingerprinting via synonym-ending-connective substitution, SHA-256 timestamped evidence packages, and punctuation-anchored micro-marks. Each layer uses a different Unicode category, so attacks on one cannot eliminate the others. Full bilingual support, zero readability impact.
34-Attack Defense
7 categories, 34 attacks simulated: Unicode normalization, invisible character removal, homoglyph substitution (9,619 confusables), and AI rewriting. Each scored on Signal (watermark survival) + Trace (forensic evidence of attack) β proving deliberate removal even when watermarks are destroyed.
Image & Video
Images: DCT frequency-domain watermarks surviving JPEG compression and resize. Videos: keyframe watermarking with temporal propagation and majority-vote extraction. Both support pre-embed similarity detection.
Who Is This For
Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
FINAL-Bench/security-scan
Most generative AI training data is crawled without consent. Your text gets summarized, images reprocessed, videos clipped β with no way to prove you're the original creator. Existing watermarks are either visible or wiped out by a single AI preprocessing pass.
Detect Before, Track After
Pre-embed β Detect theft without any watermark. Text plagiarism check, image similarity analysis (perceptual hash, SSIM, color histogram, feature matching), and video temporal matching catch copies, edits, and excerpts.
Post-embed β Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence.
Text: 4 Independent Layers
Four mechanisms work simultaneously: zero-width Unicode characters at morpheme/word boundaries (Korean Kiwi + English NLP), style fingerprinting via synonym-ending-connective substitution, SHA-256 timestamped evidence packages, and punctuation-anchored micro-marks. Each layer uses a different Unicode category, so attacks on one cannot eliminate the others. Full bilingual support, zero readability impact.
34-Attack Defense
7 categories, 34 attacks simulated: Unicode normalization, invisible character removal, homoglyph substitution (9,619 confusables), and AI rewriting. Each scored on Signal (watermark survival) + Trace (forensic evidence of attack) β proving deliberate removal even when watermarks are destroyed.
Image & Video
Images: DCT frequency-domain watermarks surviving JPEG compression and resize. Videos: keyframe watermarking with temporal propagation and majority-vote extraction. Both support pre-embed similarity detection.
Who Is This For
Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
- 3 replies
π© Report: Illegal or restricted content
1
#1 opened 19 days ago
by
pancake37812
updated a Space 19 days ago
reacted to SeaWolf-AI's post with π 20 days ago
Post
4102
Do Bubbles Form When Tens of Thousands of AIs Simulate Capitalism?
We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena β tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.
Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump
NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).
Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding β this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.
Individual intelligence does not guarantee collective intelligence.
Dataset & Paper:
FINAL-Bench/Metacognitive
We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena β tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.
Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump
NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).
Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding β this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.
Individual intelligence does not guarantee collective intelligence.
Dataset & Paper:
FINAL-Bench/Metacognitive
- 1 reply