New Technique to Deeply Poison AI on Images and Prove Creative Provenance
I've developed a new method to protect creative work from unauthorized AI training. My Poisonous Shield for Images algorithm embeds a deep, removal-resistant poison into the mathematical structure of your images. It's designed to be toxic to machine learning models, achieving up to 20-348% disruption in AI training convergence in benchmark tests.
Unlike traditional watermarks, this protection survives compression and resizing and is not removed by standard tools. The technique also embeds cryptographic proof of provenance directly into the image, verifying ownership and detecting tampering.
You can see examples and learn more about how and WHY it works better than current methods:
If you are interested in using this technology to protect your work from AI training and unauthorized use, please reach out to me. It is currently in the prototype phase but fully functioning and effective. Still working on expanding it to a production-grade usable app.
This is not intended as a pure self-promotion post. I am genuinely wanting to help creators and want to gauge interest from different communities. I've spent the past year and a half building this from scratch with new math and code to try and solve this massive problem.
I’ve ported the BDH ( https://github.com/pathwaycom/bdh ) model to MLX for Apple Silicon. It’s a faithful conversion of the PyTorch version: same math, same architecture (byte-level vocab, shared weights across layers, ReLU sparsity, RoPE attention with Q=K), with MLX-friendly APIs and a detailed README explaining the few API-level differences and why results are equivalent.
Code, docs, and training script are ready to use. You may need to adjust the training script a bit to fit your own custom dataset. Only tested on M4 so far, but should work perfect for any M1/M2/M3 users out there.
I’m currently training this MLX build on my Internal Knowledge Map (IKM) dataset Severian/Internal-Knowledge-Map Training’s underway; expect a day or so before I publish weights. When it’s done, I’ll upload the checkpoint to Hugging Face for anyone to test.
🎮 Live Model Demo: Upload an Android Screenshot and instructions to see the model in action ! Tonic/l-operator-demo
Built in a garage, funded by pre-orders, no VC. Now we’re scaling to 1 k installer units.
We’re giving 50 limited-edition prototypes to investors , installers & researchers who want to co-design the sovereign smart home.
👇 Drop “EUSKERA” in the comments if you want an invite, tag a friend who still thinks Alexa is “convenient,” and smash ♥️ if AI should belong to people - not servers.
Just wanted to annouce 🏭SmolFactory : it's the quickest and best way to finetune SmolLM3 and GPT-OSS-20B on huggingface !
Basicaly it's an app you can run on huggingface by duplicating the space and running your training directly on huggingface GPUs .
It will help you basically select datasets and models, fine tune your model , make an experiment tracker you can use on your mobile phone , push all your model card and even automatically make a demo for you on huggingface so you can directly test it out when it's done !
🌲🍄 LLM Forest Orchestra: Turning Hidden States into Music
Hello everyone! I'm excited to introduce a new Space I've been developing called LLM Forest Orchestra. This project converts the hidden states and attention patterns of transformer models into layered MIDI compositions. The concept draws inspiration from mushrooms and mycelial networks in forests. Fungi create underground connections linking plants and trees, establishing what some call a "wood-wide web" where signals and nutrients travel. Researchers have discovered that these exchanges form patterns resembling rhythms and pulses. When translated appropriately, these patterns can become music.
Transformers operate through remarkably similar principles: tokens share signals via hidden states and attention heads. This Space transforms those invisible information flows into notes, chords, and rhythms, treating the model as a digital forest orchestra.
🎛 Features
* Two compute modes: - Full model operates on a Hugging Face model (defaulting to unsloth/Qwen3-14B-Base). - Mock latents provides a CPU-friendly option that simulates tensors for immediate experimentation. * Musical controls: You can adjust scale selection, tempo grid, velocity range, instrument/role presets, and seed randomization. * Output: The system generates .mid files compatible with DAWs and remixing workflows.
🌌 Why?
Neural networks already resemble unusual musical instruments: signals flow through them, patterns emerge organically, and careful observation reveals hidden melodies. This is analogous to the forest's secret orchestra of mushrooms and trees.
👉 Try it
Try the Space here: Locutusque/LLM-Forest-Orchestra. I'm excited to hear the sounds you can generate. Please share your created MIDIs or remixes in the comments. Let's explore how this hidden forest of transformers can sound together. 🌳🎶
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
I couldn't watch innocent people get their rights trampled anymore. So I built something to help.
Stories of families torn apart, U.S. citizens detained for hours, people arrested just for speaking Spanish. This isn't the America I believe in.
Instead of doom-scrolling, I spent a few days building FIREWATCH - a free civil rights protection app.
What it does: • Real-time ICE raid alerts • Know Your Rights education in 10+ languages • Secure evidence recording • Emergency panic button • Legal hotlines and resources • 100% private, no tracking
The catch? There isn't one. You just need a free Google API key that stays on your device. Works completely offline.
So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :
basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!
this is terrible ! but the good news is that we can do something about it !
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details - **Model Name**: [lettucedect-large-modernbert-en-v1](KRLabsOrg/lettucedect-large-modernbert-en-v1) - **Organization**: [KRLabsOrg](KRLabsOrg) - **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect) - **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens - **Task**: Token Classification / Hallucination Detection - **Training Dataset**: [RagTruth](wandb/RAGTruth-processed) - **Language**: English - **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
🎉 Exciting news, everyone! I've just released **Thespis-Llama-3.1-8B**, a new language model designed for enhanced roleplaying! ✨️
It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! 🧠
Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! ✍️
Computational Model for Symbolic Representations: An Interaction Framework for Human-AI Collaboration
Hey everyone. I need your help to see if this concept, scientific logic, and testing with prompts can invalidate or validate it. My goal isn’t to make any bold statements or claims about AI, I just really want to know if I’ve stumbled upon something that can be useful in AI interactions. Here’s my proposal in a nutshell:
The Computational Model for Symbolic Representations Framework introduces a method for enhancing human-AI collaboration by assigning user-defined symbolic representations (glyphs) to guide interactions with computational models. This interaction and syntax is called Glyph Code-Prompting. Glyphs function as conceptual tags or anchors, representing abstract ideas, storytelling elements, or domains of focus (e.g., pacing, character development, thematic resonance). Users can steer the AI’s focus within specific conceptual domains by using these symbols, creating a shared framework for dynamic collaboration. Glyphs do not alter the underlying
The Core Point: Glyphs, acting as collaboratively defined symbols linking related concepts, add a layer of multidimensional semantic richness to user-AI interactions by serving as contextual anchors that guide the AI's focus. This enhances the AI's ability to generate more nuanced and contextually appropriate responses. For instance, a symbol like ! can carry multidimensional semantic meaning and connections, demonstrating the practical value of glyphs in conveying complex intentions efficiently.
