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AdinaY 
posted an update 2 days ago
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2247
GLM-4.6 is here🚀

zai-org/GLM-4.6

✨ 200K context window
✨ Superior coding & polished UI generation
✨ Stronger reasoning & tool use
✨ More capable agents & agent frameworks
YerbaPage 
posted an update about 21 hours ago
andywu-kby 
posted an update 2 days ago
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2101
Hello everyone,
I hope you’re doing well.

We’re currently developing a chatbot that can analyze and forecast sales directly from Excel files. Do you think this would be useful?

Miragic-AI/Miragic-Sales-Pilot

Please share your feedback by 👍 or 👎 this post.

Best regards,
giadap 
posted an update 4 days ago
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10416
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.

In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.

✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.

Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.

Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
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Sri-Vigneshwar-DJ 
posted an update 2 days ago
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3092
🚀 Qwen3-Omni for Marketing: A Game-Changer

Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.

What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.

Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.

The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.

Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.

Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.

Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.

Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke

Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!

#MultimodalAI #MarTech #OpenSource
Parveshiiii 
posted an update 2 days ago
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2830
🚀 Big news from XenArcAI!

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

🔗 Hugging Face: XenArcAI/Bhagwat-Gita-Infinity

Let’s bring timeless wisdom into modern AI together 🙌
SelmaNajih001 
posted an update 2 days ago
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1438
Introducing SelmaNajih001/StockPredictionExplanation, built with GRPO and RAG:
-GRPO trains the model to predict and explain stock direction.
-RAG grounds explanations in historical financial news and central bank speeches.
Together, they create a system that forecasts stock movements and shows the reasoning behind them.
Full article: Explainable Financial Predictions — https://huggingface.co/blog/SelmaNajih001/explainable-financial-predictions
Try it here: StockPredictionExplanation Space — SelmaNajih001/StockPredictionExplanation
hba123 
posted an update 3 days ago
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2090
Hey, amazing, awesome people of the beautiful internet 😍🥰

Distillation has been (from my point of view) a main driving factor for the success of hashtag#LLMs - like distilling the knowledge of an amazing big model (say hashtag#DeepSeekv3, or hashtag#GeminiAI) into yours.

Probably, you have done it with minimising a KL divergence, and it somehow worked.

Well, not that well, right?

1️⃣ Your model tends to memorise!
2️⃣ Your model might get the right answer, but its reasoning might be flawed.

To fix those problems, we rethink distillation and process a new approach! A method that is based on constrained RL that comes with nice theoretical guarantees and excellent performance!

Check it out: Rethinking Large Language Model Distillation: A Constrained Markov Decision Process Perspective (2509.22921)

Let us do distillation right! Please upvote if you find it useful!
Znilsson 
posted an update 3 days ago
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1467
Hi there, I'm an amateur AI enthusiast. I've managed to gather a fairly large dataset of .pdfs of varying quality. I have about 10gb total. I want to use it to fine tune a model and/or a RAG system. I've been having a terrible time trying to get the data prepared so I can train with it. I've tried google collab, I've tried Kaggle and paid for GPUs on both, but I'm struggling. This is because I'm not a data scientist and have zero background in programming, etc. I'm just using AI to teach me along the way. I'm willing to pay some money for GPU power, but because I fail so frequently I fear I'm just burning through it. I don't have enough horsepower on my laptop - I've tried to train on it, but to no avail. I've had minor successes, but have not been able to complete the dataset with anything comprehensible. Any and all suggestions are welcome. I need some serious help. Thanks!!
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MonsterMMORPG 
posted an update 3 days ago
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1636
Now You Can Edit Word Documents and Excel Spreadsheets With Claude AI and Wan Training Has Arrived. Also tell me what you think about voice quality.

https://youtu.be/6cH-6JaBMnM
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