Vortex Next Gen Trends

New announcements from DeepSeek Optimized Parallelism Strategies 1. DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training. 2. EPLB - an expert-parallel load balancer for V3/R1. 3. Analyze computation-communication overlap in V3/R1.

DeepSeek makes 2 major announcements 1. Starting today, DeepSeek is offering significant discounts on their API Platform during off-peak hours (16:30-00:30 UTC daily): • DeepSeek-V3: 50% OFF • DeepSeek-R1: Massive 75% OFF This means you can access powerful AI models at a fraction of the cost during these hours. For example, DeepSeek-R1 output cost drops from $2.19 to just $0.550 per 1M tokens! 2. DeepSeek has also released DeepGEMM - an impressive FP8 GEMM library that supports both dense and MoE GEMMs, powering their V3/R1 models. Key features: - Up to 1350+ FP8 TFLOPS on Hopper GPUs - Lightweight with no heavy dependencies - Fully Just-In-Time compiled - Core logic at just ~300 lines of code - Outperforms expert-tuned kernels on most matrix sizes - Supports dense layout and two MoE layouts

AI models now handle voice/speech yet building with them in Python is very frustrating FastRTC is here to solve - Automatic Voice Detection - Handling WebRTC & the backend for real-time apps - Calling Phones Github

Anthropic to release Claude Sonnet 3.7 on Feb 26 It’s expected to have step-by-step thinking, never before seen coding capabilities and web search. The best coding model which powers Cursor and Windsurf is about to get a whole lot better. Claude 3.7 Sonnet is Anthropic's most intelligent model to date and the first Claude model to offer extended thinking - the ability to solve complex problems with careful, step-by-step reasoning. Anthropic is the first AI lab to introduce a single model where users can balance speed and quality by choosing between standard thinking for near-instant responses or extended thinking or advanced reasoning. Claude 3.7 Sonnet is state-of-the-art for coding, and delivers advancements in computer use, agentic capabilities, complex reasoning, and content generation. With frontier performance and more control over speed, Claude 3.7 Sonnet is the ideal choice for powering AI agents, especially customer-facing agents, and complex AI workflows. Supported use cases: RAG or search & retrieval over vast amounts of knowledge, product recommendations, forecasting, targeted marketing, code generation, quality control, parse text from images, agentic computer use, content generation Model attributes: Reasoning, Text generation, Code generation, Rich text formatting, Agentic computer use

A new android has been created in Norway — NEO Gamma. The mechanical servant from 1X Technologies will do housework and bring coffee to its owners. Robots do the hard work, not humans

The Pika neural network has introduced the Pikaswaps feature, which changes objects in a video to any kind of thing. You can replace a pancake with a human face, a dog with an iguana, or your hand with a cyber prosthesis. Special effects are no longer needed

Which side are you on? ❤️Robots that look like robots 👍Robots that look like humans

Wow, DeepSeek announced Day 0: Warming up for OpenSourceWeek Starting next week, they'll be open-sourcing 5 repos, sharing sincere progress with full transparency. These humble building blocks in their online service have been documented, deployed and battle-tested in production. Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.

HuggingFace released the "Ultra-Scale Playbook" A free, open-source, book to learn everything about 5D parallelism, ZeRO, fast CUDA kernels, how and why overlap compute & communication – all scaling bottlenecks and tools introduced with motivation, theory, interactive plots from our 4000+ scaling experiments and even NotebookLM podcasters to tag along with you. - How was DeepSeek trained for $5M only? - Why did Mistral trained an MoE? - Why is PyTorch native Data Parallelism implementation so complex under the hood? - What are all the parallelism techniques and why were they invented? - Should I use ZeRO-3 or Pipeline Parallelism when scaling and what's the story behind both techniques? - What is this Context Parallelism that Meta used to train Llama 3? Is it different from Sequence Parallelism? - What is FP8? how does it compares to BF16? The largest factor for democratizing AI will always be teaching everyone how to build AI and in particular how to create, train and fine-tune high performance models. In other word making accessible to everybody the techniques that power all recent large language models and efficient training is possibly one of the most essential of them.

Breakthrough in Robot Design: Universal Controllers Transform How We Build Robots? Northwestern University researchers have made a significant breakthrough in robotics design, introducing a method that could revolutionize how we create and evolve robots. Their paper "Accelerated co-design of robots through morphological pretraining" presents a novel approach that solves a decades-old challenge in robotics. Code is coming soon. And here are more robots Key Innovations: 1. Universal Controller - Developed a single controller that can work with multiple robot body types - Pre-trained on millions of different robot morphologies - Uses gradient-based optimization through differentiable simulation - Can immediately adapt to new robot designs without extensive retraining 2. Zero-Shot Evolution - Allows rapid testing of new robot body designs - Enables immediate evaluation of design changes - Supports successful recombination of robot parts - Dramatically speeds up the design process 3. Diversity Maintenance - Identified and solved "diversity collapse" - a previously unknown problem in robot co-design - Developed methods to maintain morphological diversity while improving performance - Enabled successful crossover between different robot designs Technical Details: - Controllers are trained on over 10 million distinct robot morphologies - Uses differentiable simulation for gradient-based optimization - Supports complex 3D environments with varying terrains - Enables robots to perform adaptive behaviors like phototaxis (movement toward light) Future Implications: - Could dramatically accelerate robot design and development - Opens new possibilities for self-reconfigurable robots - Provides a framework for more complex multi-material robots - May help bridge the simulation-to-reality gap in robotics

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xAI announces Grok 3. Here is everything you need to know Elon mentioned that Grok 3 is an order of magnitude more capable than Grok 2. Total GPUs: 200K The capacity was doubled in 92 days! All of this compute was used to improve Grok -- which has lead to Grok 3. Grok 3 involved 10x more training than Grok 2! Grok finished pretraining in early January! The model is still training. Here are the benchmark numbers: Grok 3 significantly outperforms other models in its category such as Gemini 2 Pro and GPT-4o. Even Grok-3 mini shows to be competitive. Results of early Grok 3 in the Chatbot Arena (LMSYS) It reached an Elo score of 1400 which no other model has achieved. The model score keeps improving. Grok 3 also has reasoning capabilities too! The Grok team has been testing these capabilities which they have unlocked using RL. The model is good, especially in coding. Grok 3 coding example: Thinking traces as generated as the model tries to solve the problem. Elon confirmed that the thinking steps have been obscured to avoid getting copied. Grok 3 also excels at creative coding like generating creative and novel games. Elon emphasized Grok 3's creative emergent capabilities. You can also use the Big Brain mode to use more compute and reasoning with Grok 3. Grok 3 Reasoning performance: The results correspond to the beta version of Grok-3 Reasoning. It outperforms o1 and DeepSeek-R1 when given more test-time compute (allowing it to think longer). The Grok 3 mini reasoning model is also very capable. Grok 3 Reasoning Beta performance on AIME 2025. Grok 3 shows generalization capabilities. It not only does coding and math problem-solving, but it can also do other creative and useful real-world tasks. One of the results generated with Grok 3 mini. Bejeweled Tetris generated by Grok 3. Grok 3 cannot only unlock test-time compute, it also enables capable agents. These capabilities have led to a new product called DeepSearch. "Next generation of search agents to understand the universe". More on DeepSearch: - the model can think deeply about user intent - what facts to consider - how many websites to browse - it can cross-validate different sources. DeepSearch also exposes the steps that it takes to conduct the search itself. Improvements will happen rapidly and almost daily according to the team. There is also a Grok-powered voice app coming too -- about a week away! Open-source approach: The last version will be open-sourced when the most recent version is fully out. After Grok 3 stable version is out, it is highly likely Grok 2 will be open-sourced. (within a few months). SuperGrok dedicated app is also available with a polished experience. Try on the web as well: grok.com The web will include the latest Grok features.

DeepSeek introduced CodeI/O, a new method that helps AI learn reasoning patterns hidden in code Models train to predict inputs and outputs of given code, all while explaining its reasoning with Chain-of-Thought (CoT) in natural language. CodeI/O improves models' general reasoning skills, such as: - planning steps logically - searching for solutions - breaking problems into smaller parts DeepSeek gathered over 810,000 Python code files from different sources to cover a big variety of reasoning styles, like puzzles, math problems, etc. Then they cleaned and structured it into a unified format using DeepSeek-V2.5, ran the code and collected input-output pairs. CODEI/O++: Improving training with multi-step feedback It's an improved dataset where the model learns not just from its successes but also from its mistakes. To create it, researchers used a feedback loop: - If the model gets an answer wrong, it's told why it was wrong and tries again. - If the function fails to run, researchers include that feedback too. - The model then revises its response and tries again. This extra training step makes models even more accurate. Data and models.

Google DeepMind released a short course on AGI safety The course offers a concise and accessible introduction to AI alignment problems and our technical & governance approaches, consisting of short recorded talks and exercises (75 minutes total).

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OpenAI released its Reasoning model best practices 1. Use delimiters for clarity: Use delimiters like markdown, XML tags, and section titles to clearly indicate distinct parts of the input, helping the model interpret different sections appropriately. 2. The guide differentiates between reasoning models (e.g., o1, o3-mini) and GPT models (e.g., GPT-4o). Reasoning models are built for complex, multi-step tasks—such as planning, detailed document analysis, and visual interpretation—while GPT models focus on speed and cost efficiency for well-defined tasks. - In practice, reasoning models excel at clarifying ambiguous prompts, extracting key details from extensive unstructured data, and performing multi-step planning or code review. - They are best used with clear, concise prompts that include explicit constraints and delimiters; - elaborate chain-of-thought instructions are unnecessary since these models reason internally.

GREEN is a new lightweight neural network designed to how we analyze brain activity GREEN combines two powerful approaches: 1. Wavelet-based frequency filtering to capture dynamic brain rhythms 2. Riemannian geometry to decode spatial patterns in EEG signals EEG data holds secrets to brain health, cognitive function, and aging—but traditional methods often miss nuanced signals. GREEN changes the game by: - Detecting subtle brain activity changes with exceptional sensitivity - Operating on a low computational budget (ideal for real-world applications!) - Delivering interpretable outputs to link findings to brain mechanisms. GitHub.

OpenAI roadmap update for GPT-4.5 and GPT-5 Sam Altman just dropped major news about the future of their AI development. The company is making a dramatic shift toward unified intelligence with GPT-4.5 and GPT-5. Key highlights from the announcement: • GPT-4.5 (codenamed "Orion") will be their final traditional model before a complete system overhaul • GPT-5 represents a groundbreaking merger of all OpenAI technologies, including o3, marking the end of standalone models • The most surprising part? Free ChatGPT users will get unlimited access to GPT-5's standard intelligence setting They're moving toward what they call "magic unified intelligence" - essentially making AI that "just works" without users needing to understand the technical details. Premium features for Plus and Pro users will include enhanced intelligence levels plus access to voice, canvas, search, and deep research capabilities.

HuggingFace released 8GB of high quality reasoning math They temporarily commandeered the HF cluster to generate 1.2 million reasoning-filled solutions for 500,000 NuminaMath problems using the DeepSeek-R1 model. This is significant for AI development because: 1. It creates a large dataset of mathematical reasoning examples 2. These solutions can be used to train future AI models 3. It demonstrates the current capabilities of AI in solving complex mathematical problems Datasets.