# train Published articles for train. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data DevFeed: [Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data]() Original publisher: [Read original article]() Published: 2026-09-10T10:42:55Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [model-deployment](), [Inference](), [data](), [datasets]() Tags: [ai](), [ai-models](), [cloud](), [data](), [enterprise](), [inference](), [on-prem](), [partnership](), [train]() ### AI overview Cloudera and Mistral announce a partnership to deploy and train customized AI models on enterprise data across hybrid, on-premises, cloud, and air-gapped environments while retaining data control. ### Source excerpt Cloudera and Mistral join forces to bring specialized, sovereign AI intelligence to enterprise data, helping regulated industries innovate on their own terms. ## Quiz: Python AI: How to Build a Neural Network & Make Predictions DevFeed: [Quiz: Python AI: How to Build a Neural Network & Make Predictions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-03T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Neural Network](), [Artificial Intelligence](), [math](), [dataset]() Tags: [ai](), [math](), [neural-networks](), [predictions](), [python](), [train](), [training]() ### AI overview An interactive 13-question quiz testing understanding of how to build a neural network and make predictions with Python AI. It covers input vectors, layers, weights, bias, dot products, sigmoid activation, mean squared error, and backpropagation. ### Source excerpt Check your grasp of how neural networks make predictions in Python, from dot products and activation functions to gradient descent and backpropagation. ## Build Your Own Face Recognition Tool With Python DevFeed: [Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Kyle Stratis Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Machine learning](), [object-detection](), [Training AI Models](), [Command-line interface]() Tags: [command-line](), [computer-vision](), [machine-learning](), [python](), [testing](), [train](), [tutorial]() ### AI overview A Python tutorial for building a command-line face-recognition tool that detects faces in images, trains and validates a model, and labels detected faces with bounding boxes. ### Source excerpt In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish! ## Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets DevFeed: [Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets]() Original publisher: [Read original article]() Author: Sundar Raghavan; Steven Palma; Cagatay Cali; Arron Bailiss; Yin Song Published: 2026-08-13T17:16:04Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [lerobot](), [hugging face](), [cloud-infrastructure](), [dataset](), [SDKs](), [Command-line interface](), [Open Source]() Tags: [agents](), [apache](), [aws](), [cli](), [code](), [data](), [datasets](), [hub](), [hugging-face](), [lerobot](), [models](), [open](), [open-source](), [robots](), [storage](), [strands-agents](), [train](), [xet]() ### AI overview This article describes a continuous robotics data loop using Strands Agents, LeRobot, Hugging Face Hub, and Hugging Face Storage Buckets. It covers recording demonstrations, collecting episodes, training policies on growing datasets, deploying checkpoints, and using mutable Xet-backed storage to reduce repeated data transfers. ### Source excerpt Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets You have an agent that can already record a demonstration and push it to the Hugging Face Hub. Now you want to run that loop continuously: collect episodes through the day, train a policy on the growing dataset, deploy it, and pull the next batch back to improve it. Run that loop once and every piece works. Run it every day and you start paying for the same byte transfers over and over. ## Orchard: An open framework for scalable agentic AI DevFeed: [Orchard: An open framework for scalable agentic AI]() Original publisher: [Read original article]() Author: Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao Published: 2026-08-03T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Artificial Intelligence](), [AI Research](), [Open Source](), [Frameworks](), [Kubernetes](), [Reinforcement learning](), [computer-use](), [codex](), [OpenClaw]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [autonomous](), [autonomous-agents](), [building](), [codex](), [evaluation](), [framework](), [infrastructure](), [kubernetes](), [learning](), [models](), [open](), [open-source](), [research](), [research-blog](), [train](), [workflows]() ### AI overview Orchard is an open-source framework for training and evaluating agentic AI systems across software engineering, web navigation, and personal-assistant tasks. Its reusable Orchard Env provides Kubernetes-based infrastructure for data collection, reinforcement-learning rollouts, and evaluation, while Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate strong results from relatively small open-weight models. ### Source excerpt Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research. ## How controllers from industrial machinery can coordinate multitask machine learning DevFeed: [How controllers from industrial machinery can coordinate multitask machine learning]() Original publisher: [Read original article]() Author: Theodore Vasiloudis Published: 2026-07-30T17:26:47Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Machine Learning & Artificial Intelligence](), [Graphs](), [Training AI Models](), [Neural Network](), [data](), [Algorithm](), [systems]() Tags: [data](), [machine-learning](), [model](), [multitask-learning](), [research](), [self-supervised-learning](), [train](), [training]() ### AI overview ControlG addresses conflicting objectives in graph self-supervised learning by allocating computational capacity to one objective at a time and using a proportional-integral-derivative controller to select which objective receives attention next. ### Source excerpt Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. ## Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes DevFeed: [Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes]() Original publisher: [Read original article]() Author: Chris Alexiuk Published: 2026-07-23T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [rlvr](), [Large Language Model](), [Fine-tuning](), [Reinforcement learning](), [Python](), [coding](), [math]() Tags: [agentic-ai-generative-ai](), [blog](), [coding](), [customization](), [developer-tools-techniques](), [developers](), [experiment](), [featured](), [fine-tuning](), [getting-started](), [math](), [nemotron](), [nvidia](), [open](), [python](), [reinforcement-learning](), [rlvr](), [top-stories](), [train](), [training](), [tutorial]() ### AI overview This tutorial shows how to customize NVIDIA Nemotron 3 Nano with Prime Intellect Lab using reinforcement learning with verifiable rewards on a Python Math environment. It covers a baseline-training-reevaluation workflow and produces a downloadable LoRA adapter. ### Source excerpt Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a... ## Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72 DevFeed: [Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72]() Original publisher: [Read original article]() Author: Kirthi Devleker Published: 2026-07-21T18:30:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [GPU](), [Nvidia](), [AI Platform](), [NCCL](), [networking](), [deepseek]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-platform](), [ai-training](), [collective](), [communication](), [compute](), [data-center-cloud](), [deepseek](), [developer-tools-techniques](), [efficiency](), [featured](), [frontier-model](), [gb300-nvl72](), [gpu](), [infrastructure](), [latency](), [llm-techniques](), [low-latency](), [megatron](), [mixture-of-experts-moe](), [moe](), [nvidia](), [nvl72](), [performance](), [top-stories](), [train](), [training-ai-models]() ### AI overview The article explains how NVIDIA GB300 NVL72 achieved a world record for DeepSeek-V3 671B mixture-of-experts pre-training. It focuses on the communication demands of MoE models, including all-to-all traffic between GPUs, and the need for tightly coupled scale-up and predictable scale-out networking to sustain delivered training performance. ### Source excerpt Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token... ## GPT-Red: Unlocking Self-Improvement for Robustness DevFeed: [GPT-Red: Unlocking Self-Improvement for Robustness]() Original publisher: [Read original article]() Published: 2026-07-15T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ai safety](), [OpenAI](), [Vulnerabilities](), [Responsibility & Safety](), [Training AI Models](), [Model Development](), [Deployment](), [Monitoring](), [Bug Bounty](), [browsers](), [data]() Tags: [ai](), [ai-safety](), [attacks](), [browsers](), [code](), [data](), [deployment](), [gpt](), [model](), [models](), [monitoring](), [openai](), [prompt-injection](), [safety](), [tool](), [tools](), [train](), [training](), [vulnerabilities]() ### AI overview OpenAI describes GPT-Red, an automated red-teaming model that uses adversarial attacks and self-play to find vulnerabilities, generate training data, and improve the robustness of future AI systems against prompt injection. The approach complements human and third-party red-teaming, layered safeguards, and real-time monitoring. ### Source excerpt Explore GPT-Red, OpenAI's automated red teaming system that uses self-play to improve AI safety, alignment, and prompt injection robustness. ## NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x DevFeed: [NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-13T19:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Ising](), [Nvidia](), [Artificial Intelligence](), [Neural Network](), [real-time](), [Latency](), [Math and Logic]() Tags: [3d](), [agentic-ai-generative-ai](), [ai](), [ai-foundation-models](), [data-center-cloud](), [developer-tools-techniques](), [featured](), [hpc-scientific-computing](), [ising](), [latency](), [nvidia](), [quantum](), [quantum-computing](), [real-time](), [scale](), [train](), [training]() ### AI overview NVIDIA's Ising Decoder ColorCode 1 Fast is presented as a decoder for triangular color codes that improves logical error rates by more than 347.7x and runtime by 7.3x compared with Chromobius at d=31 and a 0.3% physical error rate. The article also describes an Ising decoding training pipeline for small 3D CNN-based pre-decoders supporting real-time quantum error correction operations. ### Source excerpt Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes... ## SensorFM: Towards a general intelligence and interface for wearable health data DevFeed: [SensorFM: Towards a general intelligence and interface for wearable health data]() Original publisher: [Read original article]() Published: 2026-07-09T09:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [multimodal](), [data](), [Google]() Tags: [ai](), [data](), [generative-ai](), [google](), [health](), [machine-intelligence](), [model](), [models](), [multimodal](), [research](), [train](), [training]() ### AI overview Google Research introduces SensorFM, a large sensor foundation model trained on more than one trillion minutes of de-identified, multimodal wearable data from five million consented participants. The model learns reusable representations of human physiology that transfer across health prediction tasks and support label-efficient adaptation and data infilling. ### Source excerpt Generative AI ## 10 Confusing LLM Concepts, Explained Simply DevFeed: [10 Confusing LLM Concepts, Explained Simply]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-06-01T15:52:18Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [Large Language Model](), [Artificial Intelligence](), [AI Agent](), [grpo](), [dataset](), [coding](), [math]() Tags: [agent](), [ai](), [artificial-intelligence](), [coding](), [cpu](), [data-science](), [dataset](), [gpu](), [grpo](), [llm](), [llms](), [machine-learning](), [math](), [programming](), [quantization](), [technology](), [tpu](), [train](), [training]() ### AI overview This tutorial introduces LLM concepts including on-policy and off-policy learning. It explains how models generate, score, and learn from responses, including the use of GRPO, teacher models, and datasets. The supplied excerpt also identifies CPU, GPU, TPU, pruning, and quantization as covered topics. ### Source excerpt The role of CPU/ GPU/ TPU in LLM workflows, Pruning, Quantization, and more. Continue reading on Level Up Coding " ## Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL DevFeed: [Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL]() Original publisher: [Read original article]() Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Lewis Tunstall; Edward Beeching; Albert Villanova del Moral; Leandro von Werra; Sergio Paniego Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [NCCL]() Tags: [artificial-intelligence](), [compute](), [hub](), [hugging-face](), [inference](), [llm](), [model](), [models](), [nccl](), [open-source](), [payload](), [policy](), [rl](), [space](), [storage](), [sync](), [train](), [training](), [trl](), [update](), [vllm]() ### AI overview The article describes delta weight synchronization for asynchronous reinforcement-learning training. A TRL change stores only modified model weights in sparse safetensors files and lets vLLM fetch them from a Hugging Face bucket, reducing transfer payloads and enabling disaggregated training without a shared cluster. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use DevFeed: [Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-05-21T16:01:00Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [data](), [recommendation systems](), [datasets](), [Machine learning](), [Embeddings](), [Feature Engineering](), [Inference](), [Latency](), [systems]() Tags: [data](), [data-infrastructure](), [datasets](), [embeddings](), [engineering](), [feature-engineering](), [inference](), [latency](), [machine-learning](), [pinterest](), [production](), [recommendation-system](), [recommendation-systems](), [retrieval](), [systems](), [train](), [transformers]() ### AI overview Pinterest describes a redesign of its user-sequence platform for ranking, retrieval, and recommendation workloads. The article explains how enriched event sequences support training datasets, offline analysis, online inference, and latency-sensitive production use cases, with goals of reducing cost, improving extensibility, and simplifying debugging. ### Source excerpt Authors (listed alphabetically) Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le Zhang Core ML Infra team: Eric Shang, Pihui Wei ML Data team: Connor Votroubek, Yi He User Understanding team: Camilo Munoz, Simin Li If you work on ranking, retrieval, or recommendation systems, you've probably asked for some version of the same thing: "Give me the last N meaningful actions this user took, with the right enrichments, in a format that's easy to train and serve ML models." On paper, that sounds simple. In practice, "user sequences" often become one of the most expensive and fragile parts of the ML data stack. They end up powering everything from training datasets to offline analysis and online inference, so they need to be fresh and complete at the same time. They must remain consistent as you add new events and enrichments. And they have to do all of this while serving latency-sensitive production workloads. This article walks through how we redesigned our user-sequence platform to make these sequences cheaper to run, faster to extend, and easier to debug, while still supporting demanding production use cases. What We Mean by "User Sequence" In this context, a user sequence is an ordered list of recent, relevant events for a user, along with the enrichments (signals) attached to each event. Here, enrichments mean all the extra signals we attach to raw events, so they're useful for models: embeddings (for example, Pin or query representations), contextual features (such as surface, device, or country), and derived attributes or counters that describe how the user interacted with a piece of content over time. A concrete example helps. Imagine a sequence made up of the last 500 engagements a user had with Pinterest Pins. Each event in that sequence might carry a timestamp, an action type, the surface where the action occurred, and a handful of embedding features or categorical attributes. As a data primitive, user sequences are powerful. They capture temporal b ## Decoupled DiLoCo: A new frontier for resilient, distributed AI training DevFeed: [Decoupled DiLoCo: A new frontier for resilient, distributed AI training]() Original publisher: [Read original article]() Author: Arthur Douillard and the DiLoCo team Published: 2026-04-22T10:20:03Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Training AI Models](), [AI Infrastructure](), [Resilience](), [data centers](), [Hardware](), [Machine learning](), [networking](), [Google]() Tags: [ai](), [ai-infrastructure](), [ai-training](), [communication](), [compute](), [data-centers](), [hardware](), [infrastructure](), [networking](), [performance](), [research](), [resilience](), [scale](), [tpu](), [train]() ### AI overview Google describes Decoupled DiLoCo, a resilient distributed training architecture that trained a 12-billion-parameter model across four U.S. regions using achievable wide-area connectivity. By overlapping communication with computation, it was reported to be more than 20 times faster than conventional synchronization and could continue operating despite failures. ### Source excerpt Google's new distributed architecture keeps AI training runs on track across distant data centers, with exceptional efficiency - even when hardware fails. ## Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers DevFeed: [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen Published: 2026-04-16T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal](), [sentence-transformers](), [datasets](), [Machine learning](), [Artificial Intelligence](), [Open Source]() Tags: [community](), [data](), [datasets](), [embedding](), [evaluation](), [models](), [multimodal](), [nlp](), [open-source](), [optimization](), [performance](), [retrieval](), [sentence-transformers](), [train](), [training](), [transformers](), [vision]() ### AI overview A practical guide to fine-tuning multimodal Sentence Transformer embedding and reranker models for visual document retrieval. It explains the training pipeline and shows how domain-specific fine-tuning improved retrieval performance from 0.888 to 0.947 in the example evaluation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The OEIS meta sequence and subway stations DevFeed: [The OEIS meta sequence and subway stations]() Original publisher: [Read original article]() Published: 2026-04-09T13:55:17Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Sequences](), [math](), [Database]() Tags: [database](), [depths-of-oeis](), [new-york-city](), [numberphile](), [oeis](), [quirk](), [sequence](), [sequences](), [shortform](), [train]() ### AI overview This commentary examines OEIS sequence A051070, whose nth term is the nth entry of sequence A_n, or -1 when that sequence lacks enough terms. It highlights unusually large or unknown values, subway-stop sequences included in the OEIS, and self-referential questions involving A051070 and A102288. ### Source excerpt A051070 is a sequence about OEIS sequences. a(n) is the n-th term in sequence A_n (or -1 if A_n doesn't have enough terms). So the first term in A051070 is 1 because A000001 is the number of groups of order n, and that sequence has 1 as its entry in index 1. A000002 is the Kolakoski sequence (what? For another time) and has value 2 in entry 2. The sequence continues: 1, 2, 1, 0, 2, 3, 0, 7, 8, 4, 63, 1, 316, ... ## How We Developed Zeta2 DevFeed: [How We Developed Zeta2]() Original publisher: [Read original article]() Author: Oleksiy Syvokon, Ben Kunkle Published: 2026-04-07T00:00:00Z Content type: article Language: en Sources: [Zed Industries - Blog]() Topics: [Development](), [Code](), [Large Language Model](), [datasets](), [Testing](), [Training AI Models](), [GitHub]() Tags: [code](), [context](), [development](), [evaluation](), [examples](), [github](), [llm](), [model](), [predictions](), [synthetic](), [testing](), [train](), [training]() ### AI overview Zed describes how it developed Zeta2, a faster edit-prediction model trained through knowledge distillation. The process used richer code context, ethically collected starting states, teacher-prompt evaluation, and roughly 100,000 synthetic examples derived from GitHub commits. ### Source excerpt A deep dive into how we improved our Zeta edit predictions model, Zeta2. ## Gemini 3.1 Flash Live: Making audio AI more natural and reliable DevFeed: [Gemini 3.1 Flash Live: Making audio AI more natural and reliable]() Original publisher: [Read original article]() Author: Valeria Wu Published: 2026-03-26T15:23:35Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google AI](), [API](), [real-time](), [Benchmark](), [Google]() Tags: [ai](), [api](), [audio](), [benchmark](), [developers](), [enterprise](), [gemini](), [google](), [google-ai](), [latency](), [launch](), [model](), [none](), [real-time](), [scale](), [search](), [speed](), [train](), [voice]() ### AI overview Google DeepMind introduces Gemini 3.1 Flash Live, a real-time audio and voice model designed to make dialogue more natural, reliable, and responsive. The article highlights improved reasoning, multi-step task execution, tonal understanding, multilingual capability, lower latency, and faster responses for developers, enterprises, and users across Gemini products, the Gemini Live API, Search Live, and Gemini Enterprise for Customer Experience. ### Source excerpt Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise. ## The generative recommender behind Shopify's commerce engine DevFeed: [The generative recommender behind Shopify's commerce engine]() Original publisher: [Read original article]() Author: Yang Liu Published: 2026-02-25T16:04:54Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Temporal data](), [model-deployment](), [real-time](), [Feature Engineering]() Tags: [architecture](), [engineering](), [feature-engineering](), [generative](), [latency](), [model](), [production](), [real-time](), [scale](), [shopify](), [train](), [training]() ### AI overview Shopify describes a generative recommender that treats buyer journeys as raw event sequences. An autoregressive, causally masked model predicts next products or ads while meeting real-time production constraints at Shopify's scale. ### Source excerpt Treating buyer journeys as sequences instead of simplified signals, and building a model fast enough to serve at scale. ## Train AI models with Unsloth and Hugging Face Jobs for FREE DevFeed: [Train AI models with Unsloth and Hugging Face Jobs for FREE]() Original publisher: [Read original article]() Author: ben burtenshaw; Daniel (Unsloth); Michael Han; Maxime Labonne; Daniel van Strien; shaun smith Published: 2026-02-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [jobs](), [Fine-tuning](), [Training AI Models](), [hugging face](), [AI Models](), [Command-line interface](), [Open Source]() Tags: [agents](), [ai](), [ai-models](), [claude](), [cli](), [codex](), [documentation](), [fine-tuning](), [hub](), [hugging-face](), [jobs](), [llm](), [models](), [monitoring](), [open-source](), [pricing](), [train](), [training]() ### AI overview This article explains how to fine-tune the small LFM2.5-1.2B-Instruct language model using Unsloth and Hugging Face Jobs. It covers free credits, prerequisites, CLI-based job submission, coding-agent skills, managed cloud GPU training, monitoring, and pushing the trained model to the Hugging Face Hub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The next chapter for AI in the EU DevFeed: [The next chapter for AI in the EU]() Original publisher: [Read original article]() Published: 2026-01-28T01:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [AI Strategy](), [OpenAI](), [ChatGPT](), [data](), [Frontier AI]() Tags: [ai](), [ai-adoption](), [chatgpt](), [eu](), [europe](), [global-affairs](), [growth](), [openai](), [partnerships](), [safety](), [strategy](), [train]() ### AI overview OpenAI's EU Economic Blueprint 2.0 presents new data and initiatives to accelerate AI adoption across Europe. The plans include training 20,000 SMEs, funding research on youth safety and wellbeing, and expanding government partnerships, alongside recommendations for national education frameworks, portable skills accreditation, and adoption measurement. ### Source excerpt OpenAI launches the EU Economic Blueprint 2.0 with new data, partnerships, and initiatives to accelerate AI adoption, skills, and growth across Europe. ## ATLAS: Practical scaling laws for multilingual models DevFeed: [ATLAS: Practical scaling laws for multilingual models]() Original publisher: [Read original article]() Published: 2026-01-27T18:58:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [scaling laws](), [Language models](), [Training AI Models](), [data](), [Google](), [datasets](), [Artificial Intelligence]() Tags: [ai](), [atlas](), [data](), [generative-ai](), [global](), [google](), [language-models](), [machine-intelligence](), [models](), [natural-language-processing](), [research](), [scaling-laws](), [train]() ### AI overview ATLAS introduces adaptive scaling laws for training multilingual language models. Based on 774 runs across 400+ languages and evaluations in 48 languages, it helps practitioners choose model size, data volume, and language mixtures while optimizing performance for a target language. ### Source excerpt Generative AI ## Introducing Waypoint-1: Real-time interactive video diffusion from Overworld DevFeed: [Introducing Waypoint-1: Real-time interactive video diffusion from Overworld]() Original publisher: [Read original article]() Author: Andrew Lapp; Louis Castricato; Scott Fox; Shahbuland Matiana; David Rossi Published: 2026-01-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [World models](), [real-time](), [Artificial Intelligence](), [Inference](), [Latency](), [Streaming](), [Transformer](), [Tooling](), [AI Development](), [Open Source]() Tags: [announcement](), [artificial-intelligence](), [diffusion](), [inference](), [latency](), [model](), [models](), [open-source](), [performance](), [procedural](), [real-time](), [streaming](), [tooling](), [train](), [world-model]() ### AI overview Overworld introduces Waypoint-1, a real-time interactive video diffusion model that generates explorable worlds from frames and responds to text, mouse, and keyboard controls. The article describes its frame-causal rectified flow transformer, training on diverse video game footage, diffusion forcing, self-forcing, and the WorldEngine inference library. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()