AI and Machine Learning

⭐️ Want to become an AI/ML Engineer? Here’s a simple 15-step roadmap – from learning Python to building real-world projects.

GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model

The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.

What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.

The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.

➡️ HuggingFace

📕 RAG Pipeline vs Self RAG vs Agentic RAG

🔅 AI Model Compression Techniques: Building Cheaper, Faster, and Greener AI 📝 Learn how to make AI models faster, smaller, and more sustainable with practical techniques like pruning, quantization, and distillation. 🌐 Author: Tejas Chopra 🔰 Level: Intermediate ⏰ Duration: 1h 55m 📋 Topics: Model Compression, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

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📢 Hugging Face is now integrated with Kaggle Notebooks Starting today, Kaggle users can directly use any Hugging Face models in their notebooks — without manual downloads, token setup, or additional libraries. 🤝 Hugging Face and Kaggle platforms announced a partnership that will allow competition participants and researchers to work with the latest SOTA models literally "out of the box." 🔥 This is just the first step: teams are already working on further integration to make working with HF models even more convenient within the Kaggle ecosystem. 🔗 You can try it right now — support is already included in the Kaggle Notebooks environment. https://huggingface.co/blog/kaggle-integration

🔎 Using TensorFlow Object Detection API with OpenVINO™ 🛠 TensorFlow, or TF for short, is an open-source framework for machine learning. 🔰 The TensorFlow Object Detection API is an open-source computer vision framework built on top of TensorFlow. 🔰It is used for building object detection and instance segmentation models that can localize multiple objects in the same image. 🔰TensorFlow Object Detection API supports various architectures and models, which can be found and downloaded from the TensorFlow Hub. 🌐 Links: Github

🔅 Applied AI: Getting Started with Hugging Face Transformers 📝 Get up and running with pretrained transformers in Hugging Face, the popular platform for natural language processing (NLP) applications. 🌐 Author: Kumaran Ponnambalam 🔰 Level: Intermediate ⏰ Duration: 1h 14m 📋 Topics: Hugging Face Products, Applied Machine Learning, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

👨‍💻AFFiNE 🛠 AFFiNE is a private and open source productivity platform that combines document creation, drawing, and task planning features. 🔰Users can place a variety of elements on an infinite canvas and leverage multi-modal AI solutions while keeping data local with the ability to sync and collaborate in real time. 🔰The platform supports plugins and provides pre-installed templates, making it convenient for both individual users and organizations. 🔗Links: https://github.com/toeverything/AFFiNE

💻 AI Made Coders Slower — But Strangely Happier A new study by AI research group METR gave pro developers AI tools like Cursor… and it actually slowed them down by 19%. ⚡️ They expected a 20% speed boost — got the opposite 🤖 AI suggestions were “almost right” but took longer to fix than coding from scratch 😅 Yet devs kept using it… because it felt easier and less stressful The takeaway? AI won’t replace your skills — but it might make the job feel less painful.

💡 What Are LLMs? 📊 Large Language Models are AI systems trained on vast text data to understand and generate human-like language. 🧬 Built on transformer architecture, they predict the next word using patterns in grammar, context, and knowledge. ⚡️ From writing emails to coding and reasoning, they power tools like chatbots and assistants. 🔥 Flaws like bias exist, but they’re reshaping how machines think. Language is the new code.

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