Hey everyone! We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to: - Connect with fellow developers and AI enthusiasts - Share your projects, insights, and questions - Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
Iam very happy to announce our latest embedding model sparkembedding-300m base on embeddinggemma-300m we fine tuned it on 1m extra examples spanning over 119 languages and result is this model achieves exceptional cross lingual retrieval
We’re proud to release AIRealNet — a binary image classifier built to detect whether an image is AI-generated or a real human photograph. Based on SwinV2 and fine-tuned on the AI-vs-Real dataset, this model is optimized for high-accuracy classification across diverse visual domains.
If you care about synthetic media detection or want to explore the frontier of AI vs human realism, we’d love your support. Please like the model and try it out. Every download helps us improve and expand future versions.
We’ve just released our new dataset: **Bhagwat‑Gita‑Infinity** 🌸📖
✨ What’s inside: - Verse‑aligned Sanskrit, Hindi, and English - Clean, structured, and ready for ML/AI projects - Perfect for research, education, and open‑source exploration
🚀 New Release from XenArcAI We’re excited to introduce AIRealNet — our SwinV2‑based image classifier built to distinguish between artificial and real images.
✨ Highlights: - Backbone: SwinV2 - Input size: 256×256 - Labels: artificial vs. real - Performance: Accuracy 0.999 | F1 0.999 | Val Loss 0.0063
🎮 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 !
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.
Runway’s new **Aleph** model lets you *transform*, *edit*, and *generate* video from existing footage using just text prompts. You can remove objects, change environments, restyle shots, alter lighting, and even create entirely new camera angles, all in one tool.
1. Be clear and specific (e.g., _“Change to snowy night, keep people unchanged”_). 2. Use action verbs like _add, remove, restyle, relight_. 3. Add reference images for style or lighting.
Aleph shifts AI video from *text-to-video* to *video-to-video*, making post-production faster, more creative, and more accessible than ever.
OpenAI has launched GPT-5, a significant leap forward in AI technology that is now available to all users. The new model unifies all of OpenAI's previous developments into a single, cohesive system that automatically adapts its approach based on the complexity of the user's request. This means it can prioritize speed for simple queries or engage a deeper reasoning model for more complex problems, all without the user having to manually switch settings.
Key Features and Improvements Unified System: GPT-5 combines various models into one interface, intelligently selecting the best approach for each query.
Enhanced Coding: It's being hailed as the "strongest coding model to date," with the ability to create complex, responsive websites and applications from a single prompt.
PhD-level Reasoning: According to CEO Sam Altman, GPT-5 offers a significant jump in reasoning ability, with a much lower hallucination rate. It also performs better on academic and human-evaluated benchmarks.
New Personalities: Users can now select from four preset personalities—Cynic, Robot, Listener and Nerd to customize their chat experience.
Advanced Voice Mode: The voice mode has been improved to sound more natural and adapt its speech based on the context of the conversation.
🚀 Just Dropped: MathX-5M — Your Gateway to Math-Savvy GPTs
👨🔬 Wanna fine-tune your own GPT for math? 🧠 Building a reasoning agent that actually *thinks*? 📊 Benchmarking multi-step logic across domains?
Say hello to [**MathX-5M**](XenArcAI/MathX-5M) — a **5 million+ sample** dataset crafted for training and evaluating math reasoning models at scale.
Built by **XenArcAI**, it’s optimized for: - 🔍 Step-by-step reasoning with , , and formats - 🧮 Coverage from arithmetic to advanced algebra and geometry - 🧰 Plug-and-play with Gemma, Qwen, Mistral, and other open LLMs - 🧵 Compatible with Harmony, Alpaca, and OpenChat-style instruction formats
Whether you're prototyping a math tutor, testing agentic workflows, or just want your GPT to solve equations like a pro—**MathX-5M is your launchpad**.
All key links to OpenAI open sourced GPT OSS models (117B and 21B) which are released under apache 2.0. Here is a quick guide to explore and build with them:
I focused on showing the core steps side by side with tokenization, embedding and the transformer model layers, each highlighting the self attention and feedforward parts without getting lost in too much technical depth.
Its showing how these layers work together to understand context and generate meaningful output!
If you are curious about the architecture behind AI language models or want a clean way to explain it, hit me up, I’d love to share!