just recieved my stack of 10 floppy disks - you know what that means
floppyx4 is canceled, floppyx10 is next
here's the intended specs: - official tokenizer (the actual tokenizer for gpt-2) - actual gpu training (barely) - sharegpt (if i can afford it computationally) - full thing fitting on 10 floppy disks (not just the safetensors file) - and if needed different arch (like llama)
There's a handful of open models like Qwen Image, Flux, Wan, and others on AI image generators like VisualGPT, Pixlr, Free AI, and more. And here's the catch - they're free.
Hugging Face inference costs money just to generate simple images. Things like VisualGPT still have your favorite models for free.
GPT Image isn't worth it - and neither is HF inference. The real way to use open models is the things you closed-source third-party lovers already use.
SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!
We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
🧪 What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
We are thrilled to announce the release of **NRS_QWEN_MYTHOS_1M**, a high-performance reasoning model built on the powerful **Qwen 3.5 9B** base. At **SKT AI LABS**, we’ve applied our proprietary **Neural Reasoning System (NRS)** to push the boundaries of what a 9B model can do.
🔥 **Why this model is a Game-Changer:**
✅ **100x High Reasoning Capacity:** Deep logical thinking and complex problem-solving via NRS Boosting. ✅ **1 Million Token Context:** Handle massive codebases, long documents, and multi-turn agentic tasks with ease (YaRN Scaling). ✅ **Advanced Thinking Mode:** Native <think> tags for step-by-step Chain-of-Thought reasoning. ✅ **Tool-Use Ready:** Optimized for Python execution and Web Search with self-correction. ✅ **Blazing Fast:** Efficient 9B architecture that runs smoothly on consumer hardware (RTX 3090/4090).
🛠️ **Technical Highlights:** * **Base:** Qwen 3.5 9B * **Tuning:** NRS Specific Tuning high-quality samples. * **License:** NRS DOCS Whether you are a developer building coding agents, a researcher dealing with long-context data, or just someone who loves deep reasoning, this model is built for you.
We are excited to share that SKT-NRS is now live on Hugging Face. We’ve developed a Neural Reasoning System (NRS) designed to enhance the capabilities of foundation models — giving them stronger reasoning, improved performance, and more reliable outputs across a wide range of tasks.
Our goal is to bring meaningful quality improvements to both new and existing models. You’ll start seeing boosted versions of various models released here soon, each refined with our NRS approach.
**What to Expect* ❤️🩹
Regular releases of Neural Reasoning-enhanced models Clear focus on better reasoning and overall model quality Ongoing improvements based on community feedback
If you’d like to stay updated, feel free to follow this space — we’ll be posting the first boosted models very soon.
**Community Requests**
Have a specific model you’d like us to work on? Looking for improvements on an existing model, or have any other requests? We’re happy to hear from you. Please share your suggestions here:
LLM-based agents handle text incredibly well, but images, videos, or PDFs with visual content are hard to interpret. mm-ctx gives your CLI agent multi-modal skills.
mm-ctx is meant to feel familiar: the UNIX tools we already love (find/cat/grep/wc), rebuilt for file types LLMs can't read natively and designed to work with agents via the CLI. - mm grep "invoice #1234" ~/Downloads searches across PDFs and returns line-numbered matches - mm cat <document>.pdf returns a metadata description of the file - mm cat <photo>.jpg returns a caption of the photo - mm cat <video>.mp4 returns a caption of the video
A few things we obsessed over: ⚡ Speed: Rust core for the hot paths 🏠 Local-first, BYO model: Uses any OpenAI-compatible endpoint: Ollama, vLLM/SGLang, LMStudio with any multimodal LLM (Gemma4, Qwen3.5, GLM-4.6V). 🔗 Composable: stdin + structured outputs 🤖 Drops into any agent via mm-cli-skills: Claude Code, Codex, Gemini CLI, OpenClaw.
We’d love to hear your feedback! Especially on the CLI and what file types and workflows you would like to see next.
Are Large Language Models actually becoming more intelligent, or just better at seeming intelligent?
There is a noticeable shift happening in the LLM space.
Models today can:
Generate cleaner and more structured code. Explain complex topics in simpler ways. Maintain longer and more coherent conversations.
Yet at the same time, they still:
Produce confident hallucinations. Fail in multi-step reasoning tasks. Break under slightly unfamiliar or challenging inputs.
This raises a critical question.
Are we advancing intelligence, or optimizing presentation?
Most improvements so far seem driven by:
Larger datasets. Increased scale. Alignment techniques like RLHF.
But these do not necessarily lead to genuine reasoning ability.
What still appears fundamentally missing:
Persistent memory across interactions. True reasoning rather than pattern completion. Grounded understanding connected to real-world context.
Reliable self-correction and verification mechanisms.
If current scaling trends start to plateau, the next breakthrough will not come from doing more of the same.
So the real question for the community is:
If you were designing the next generation of AI systems, where would you focus?
A. Larger models and compute B. Higher-quality and structured data C. Agent-based systems with tool use and memory D. New architectures beyond transformers
This is not just a technical discussion. It defines where AI is actually heading over the next few years.
I am interested to hear how others are thinking about this.
Supercharge Apple’s Shortcuts using Cloudflare Workers and Gemini within minutes (and for free, up to 1,500 requests per day) ☁️✨
Hello everyone, last week, while experimenting for fun, I created an API that allows you to easily access AI models (in this case, Google's) from the Shortcut app in order to analyze data from my apps and make the most of it thanks to the generative capabilities of advanced models.
It costs me nothing, and I think it might be good to share it so that others can build on it.
In README.md, you will find everything you need to get started and put your own microservice into production, which you can call from the app’s HTTP request features.
You will simply be asked to have a free Cloudflare account and an API key obtained from Google's AI Studio.
Feel free to take a look and get back to me if you encounter any problems during deployment.
Although more and more code editors are aligning themselves with the AGENTS.md file standard, some still use specific nomenclatures that can make it difficult to maintain different configuration files when several people are working on the same project with different agents.
Bodyboard addresses this by generating canonical instructions for code helpers from a single AGENTS.md file, thereby streamlining the production of adapter outputs for Gemini CLI, Copilot, Cline, Claude, Rules, Windsurf, and OpenAI Codex integrations.
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through: - uv as a package manager, simplifying usage via a series of pre-configured make commands. - FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network). - Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects. - A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Hey all Finally it's happening. DeepGit lite is back now, running on cpu only devices. Just smartly search across Github and spin up conversational agents in the background and have grounded conversation with repositories Try it out now!!!! zamal/DeepGit
🌐 Clinical Trials Dataset now available on Hugging Face! 🧬
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
Say hallo to GermaNER 💪– a lightweight, high-accuracy NER model for German texts, powered by XLM-RoBERTa + LoRA adapters! ⚡ Fast, efficient, and open-source – perfect for tagging names, places & orgs in real-world German data. Try it now on Hugging Face 👉 fau/GermaNER
