# Training Published articles for Training. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PPO vs GRPO, Simply Explained DevFeed: [PPO vs GRPO, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-09-17T11:47:38Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Large Language Model](), [post-training](), [Reinforcement learning](), [Algorithms]() Tags: [algorithm](), [alignment](), [human-feedback](), [llm](), [llm-training](), [post-training](), [reinforcement-learning](), [training]() ### AI overview A tutorial comparing PPO and GRPO as reinforcement learning algorithms used in LLM post-training. It explains PPO, including RLHF, policy-gradient updates, and clipped token-probability changes intended to keep model behavior close to its previous version. ### Source excerpt A simple lesson on two important LLM post-training algorithms. ## How to connect AI usage to business value DevFeed: [How to connect AI usage to business value]() Original publisher: [Read original article]() Published: 2026-09-16T12:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [codex](), [data](), [Software Engineering]() Tags: [ai](), [analytics](), [business-value](), [chatgpt](), [codex](), [product](), [training]() ### AI overview This tutorial explains how ChatGPT Work and Codex analytics help administrators connect AI adoption, usage, spending, supported tasks, and outcomes to business decisions. It covers views for identifying training needs, evaluating workflows, reviewing model and tool usage, and monitoring Codex contributions and code-review activity. ### Source excerpt Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions DevFeed: [Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T18:47:23Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [Training AI Models](), [Open Source Models & Datasets](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-development](), [ai-engineering](), [ai-models](), [coding-agents](), [data](), [developer-tools](), [developers](), [training]() ### AI overview Bolt.new is testing Forge, a research preview for individual Pro subscribers that offers up to 50 times more usage of open-weight coding models in exchange for opting in to share anonymized coding sessions. The sessions may include prompts, source code, fix traces, and conversations with the coding agent, and will support an Arcee AI project to train a trillion-parameter-class open-weight model. ### Source excerpt Bolt.new, StackBlitz's browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training The post Bolt is giving developers 50x more compute. But there's a catch. appeared first on The New Stack. ## Announcing instance preference lists for Amazon SageMaker AI training jobs DevFeed: [Announcing instance preference lists for Amazon SageMaker AI training jobs]() Original publisher: [Read original article]() Author: Kanwaljit Khurmi Published: 2026-09-15T16:01:47Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [GPU](), [Model Development](), [data-processing]() Tags: [amazon-sagemaker-ai](), [announcements](), [gpu](), [intermediate-200](), [model-development](), [pipelines](), [scheduled](), [training]() ### AI overview Amazon SageMaker AI introduces instance preference lists for training and processing jobs. Users can specify up to five instance types in priority order, and SageMaker AI launches the job on the first option with available capacity, reducing manual retries and capacity monitoring. ### Source excerpt Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ## Which Video Streaming Provider Is Best for Corporate Training? DevFeed: [Which Video Streaming Provider Is Best for Corporate Training?]() Original publisher: [Read original article]() Author: Max Wilbert Published: 2026-09-14T12:40:20Z Content type: comparison Language: en Sources: [DaCast]() Topics: [Streaming](), [communications]() Tags: [compliance](), [corporate](), [cost](), [distributed](), [live-streaming](), [streaming](), [the-video-experts-blog](), [training]() ### AI overview This guide compares Brightcove, Kaltura, Panopto, and Dacast as video streaming providers for corporate training. It focuses on LMS compatibility, compliance recording retention, and cost at scale, and summarizes the providers' stated features and pricing. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Training a distributed workforce is one of the hardest logistics problems corporate L&D teams face, and live streaming video has become one of the most effective ways to solve it. Choosing the right video streaming provider for training is [...] The post Which Video Streaming Provider Is Best for Corporate Training? appeared first on Dacast. ## A study of sequence weighting at scale DevFeed: [A study of sequence weighting at scale]() Original publisher: [Read original article]() Author: Alex Renda Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [scaling laws](), [datasets](), [Deep neural networks](), [Chinchilla scaling law]() Tags: [data](), [machine-learning](), [model](), [scaling-laws](), [training]() ### AI overview The article studies how sequence weighting affects language-model training across model scales. It reports non-monotonic behavior: small-to-medium models increasingly learn data-specific patterns in proportion to sequence weights, while large models can learn all patterns in the data more independently of those weights. ### Source excerpt TL;DR: We study the scaling laws of data weighting across in-house and open-weight LMs, finding non-monotonic behavior across scales. We vary the weight assigned to sequences during training and measure how strongly the model's loss reduction on a sequence depends on the sequence's weight. Taken together, our results are consistent with a general trend: as models transition from small to medium scale, they transition from learning general patterns independent of data weight to learning data-specific patterns proportional to the data weights. As models then transition from medium to large scale they are able to learn all patterns present in the data, once again independent of data weight. ## Interpreting Pangram DevFeed: [Interpreting Pangram]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-14T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [Large Language Model](), [datasets](), [Training AI Models]() Tags: [ai](), [llm](), [model](), [openai](), [training](), [training-data]() ### AI overview The article discusses Pangram, an AI-text detector that classifies passages as human-written, AI-generated, or mixed. It explains that Pangram manufactures training data from human-authored text and LLM-generated rewrites and edits, then describes an experiment using Opus 5 to generate text intended to read as entirely AI-generated. ### Source excerpt Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it's entirely AI generated. To which David replied that these AI detectors are bogus. Now Pangram has a pretty low false positive rate, but if you have ever used an LLM as a writing assitant, you will have probably noticed that it claims your posts 100% AI, even though you don't feel like they are. Pangram itself is a trained model, that attempts to detect segments of text as being definitely human, definitely AI and a mixture of the two. If you want to know how it works, they published a paper. The short summary is that they are manufacturing its own training data by starting from collections of known human authored text. An LLM is then tasked to understand the text and write a fresh new text on the same topic. They also let the LLM perform partial edits on that original human text and through that they can pick up on these co-authored details. Pangram claims their model to have rates of 0.0041% false AI accusations and 0.34% missed AI text. So now that we know this I figured it might be fun to have an LLM re-create David's tweet. I first came up with a prompt. And when I say I came up with that prompt I in fact used an LLM to propose to me from that tweet what I might want to say for the structure. I'm sure if you ask Pangram about if the above text is AI, it will probably say so, but that's not really the point. The point is that I then used Opus 5 to generate a text which reads entirely AI generated. If you are curious, this is the prompt I used: Original prompt to generate a David Sacks like tweet Please write an 8 paragraph tweet about "Pacing the Frontier", not longer than 350 words. Context of this tweet is this post by Dario: https://darioamodei.com/post/we-must-pace-the-frontier And this tweet by Sam Altman: "I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we' ## Autonomous LLM post-training with Tunix on TPUs DevFeed: [Autonomous LLM post-training with Tunix on TPUs]() Original publisher: [Read original article]() Author: Wei Wei Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [post-training](), [AI Agent](), [Fine-tuning](), [Large Language Model](), [grpo](), [lora](), [gemma](), [Google](), [Reinforcement learning](), [dataset](), [Command-line interface]() Tags: [autonomous](), [cli](), [fine-tuning](), [gemma](), [git](), [google](), [grpo](), [llm](), [lora](), [post-training](), [reinforcement-learning](), [training]() ### AI overview This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings. ### Source excerpt Imagine going to sleep after writing a single Markdown specification and waking up to find that an A... ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation DevFeed: [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation]() Original publisher: [Read original article]() Author: Claudio Masolo Published: 2026-09-11T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [recommendation systems](), [Large Language Model](), [Language models](), [sglang](), [Latency]() Tags: [agentic-ai-architecture](), [ai](), [ai-ml-data-engineering](), [artificial-intelligence](), [large-language-models](), [latency](), [liger](), [linkedin](), [linkedin-ai-multi-teacher](), [llms](), [ml-data-engineering](), [news](), [search](), [sglang](), [training]() ### AI overview LinkedIn describes a multi-teacher distillation pipeline for AI-powered job search that trains a 0.6B-parameter ranking model. The article focuses on SGLang-based teacher serving, online and offline distillation, and training optimizations reported to produce roughly an eightfold speedup. ### Source excerpt LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo ## SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign DevFeed: [SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [multimodal](), [Transformer](), [datasets]() Tags: [benchmarks](), [drug-discovery](), [generation](), [model](), [multimodal](), [training]() ### AI overview SimpleDesign is a single-stage, end-to-end multimodal generative model for jointly designing protein sequences and three-dimensional structures. It uses Transformer-based multimodal backbones, trains directly in data space on more than 2 million sequence-structure pairs, and achieves competitive results on co-design and unconditional generation benchmarks. ### Source excerpt Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e... ## Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. DevFeed: [Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-10T19:37:30Z Content type: news Language: en Sources: [The New Stack]() Topics: [Model Development](), [Training AI Models](), [anthropic]() Tags: [ai](), [compute](), [infrastructure](), [models](), [news](), [open](), [training]() ### AI overview Mistral's EUR 3 billion Series D is presented as a bet that open-weight AI needs accompanying compute and infrastructure to reduce dependence on concentrated model and chip providers. ### Source excerpt This week, Mistral announced it raised EUR 3 billion in a Series D funding round, pushing its post-money valuation past EUR 21 The post Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. appeared first on The New Stack. ## Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. DevFeed: [Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-10T09:00:43Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-operations](), [cuopt](), [deployment](), [nemotron](), [nvidia](), [partnership](), [supply-chain](), [training]() ### AI overview Nvidia and Palantir are fine-tuning Nemotron for Nvidia supply-chain decisions as a sovereign-AI deployment. The companies say the 30B-parameter model outperforms a model 18 times larger and plan to extend lessons from the deployment to other sectors. ### Source excerpt Nvidia and Palantir announced Thursday that they're working together to bring "sovereign AI to critical supply chains," kicking off initially The post Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. appeared first on The New Stack. ## The 13 Best Video Training Platforms for Corporate L&D in 2026 DevFeed: [The 13 Best Video Training Platforms for Corporate L&D in 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-10T00:01:56Z Content type: comparison Language: en Sources: [DaCast]() Topics: [hosting](), [Learning](), [Streaming](), [Monitoring]() Tags: [2026](), [article](), [comparison](), [corporate](), [hosting](), [learning](), [platforms](), [streaming](), [the-video-experts-blog](), [training]() ### AI overview A comparison of 13 video training platforms for corporate learning and development teams in 2026. It evaluates capabilities such as LMS integration, completion tracking, compliance recording, reporting, and pricing, identifying Dacast, Kaltura, and Panopto as having the clearest corporate training fit among the listed platforms. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 For corporate L&D teams, the best video training platforms have become core infrastructure rather than a nice-to-have. They provide a reliable and interactive learning experience for onboarding, compliance, and enablement content. Leveraging educational video hosting and video-on-demand (VOD) capabilities, [...] The post The 13 Best Video Training Platforms for Corporate L&D in 2026 appeared first on Dacast. ## "It could kill us all": what Anthropic's own researchers really think about superintelligence DevFeed: ["It could kill us all": what Anthropic's own researchers really think about superintelligence]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-09T19:51:46Z Content type: news Language: en Sources: [The New Stack]() Topics: [anthropic]() Tags: [ai](), [ai-models](), [anthropic](), [openai](), [research](), [superintelligence](), [tech-culture](), [training]() ### AI overview Anthropic researchers publicly warn that aligning self-improving superintelligent AI remains unsolved. The article contrasts their assessment of future catastrophic risk with their view that current models pose low risk. ### Source excerpt On Tuesday evening, Anthropic pretraining researcher Jacob Coxon announced on X that he'd resigned. Within hours, two of his colleagues The post "It could kill us all": what Anthropic's own researchers really think about superintelligence appeared first on The New Stack. ## On-Policy Distillation, Simply Explained DevFeed: [On-Policy Distillation, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-09-09T19:22:38Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [LLM Techniques](), [Large Language Model](), [post-training](), [Reinforcement learning]() Tags: [fine-tuning](), [gpu](), [llms](), [post-training](), [training]() ### AI overview This tutorial explains on-policy distillation (OPD), a post-training method for large language models. A student model generates its own responses, which a stronger teacher scores token by token; the student is trained to reduce the reverse KL divergence between their next-token distributions. The article distinguishes OPD from reinforcement learning, conventional knowledge distillation, and supervised fine-tuning. ### Source excerpt On-Policy Distillation (OPD) has become a popular algorithm for post-training LLMs, and almost all recent open-weight LLMs (Qwen3, GLM-5.3, and Nemotron-Cascade 2) have used it to achieve amazing performance. ## This smart boxing band takes advantage of the new Arduino Nesso N1 DevFeed: [This smart boxing band takes advantage of the new Arduino Nesso N1]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-09T15:36:33Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Hardware](), [data](), [real-time](), [Artificial Intelligence](), [post-training]() Tags: [ai](), [arduino](), [boxing](), [boxing-training](), [data](), [hardware](), [imu](), [nesso-n1](), [performance](), [post-training](), [real-time](), [smart-boxing-band](), [training]() ### AI overview An Arduino Nesso N1-based smart boxing band combines punch recognition, biometric tracking, and real-time feedback. It uses a pulse sensor and the board's IMU to relate punch types and movement to heart rate, while logging data for post-training analysis. ### Source excerpt Data is now a huge part of many sports, from F1 racing to football. Presenting that data to fans is secondary to the real purpose: helping teams and athletes maximize performance. No matter what sport you enjoy, you can benefit from that kind of quantified training. Manivannan proved that by building his Smart AI Boxing [...] The post This smart boxing band takes advantage of the new Arduino Nesso N1 appeared first on Arduino Blog. ## K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced DevFeed: [K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-09T12:00:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [Open Source Models & Datasets](), [foundation-models](), [Training AI Models]() Tags: [ai](), [ai-models](), [developer-tools](), [foundation-models](), [open](), [open-source](), [release](), [training]() ### AI overview The Institute of Foundation Models introduced K2 Horizon, a six-model open-source foundation-model fleet. The article examines its promised release of training artifacts and notes that some data, code, checkpoints, and a final 32B model were not yet available at launch. ### Source excerpt Based in the Emirati capital, Abu Dhabi, the Institute of Foundation Models (IFM) introduced K2 Horizon last week. This group The post K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced appeared first on The New Stack. ## Java's age is its AI superpower DevFeed: [Java's age is its AI superpower]() Original publisher: [Read original article]() Author: Ryan Donovan Published: 2026-09-09T04:45:00Z Content type: opinion Language: en Sources: [Stack Overflow Blog]() Topics: [coding](), [Training AI Models]() Tags: [agent](), [agentic](), [ai](), [buiilding-software](), [coding](), [data](), [ecosystem](), [history](), [ibm](), [java](), [podcast](), [se-stackoverflow](), [se-tech](), [sponsored](), [training]() ### AI overview A podcast episode argues that Java's longevity, training-data footprint, libraries, and agentic tooling make it a strong fit for coding agents. ### Source excerpt Ryan welcomes Markus Eisele to the program to talk about why your coding agent should be writing Java. ## Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI DevFeed: [Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI]() Original publisher: [Read original article]() Author: Dmytro Hryshchenko, Abhijeet Dhumal Published: 2026-09-09T03:32:28Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Fine-tuning](), [voice ai](), [Training AI Models](), [Artificial Intelligence](), [distributed-training](), [GPU](), [lora](), [data](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [distributed-training](), [fine-tuning](), [gpu](), [lora](), [open-source](), [speech](), [text-to-speech](), [training](), [voice](), [workflow]() ### AI overview This tutorial explains how to fine-tune the open source Orpheus-3B text-to-speech model for Turkish using Red Hat OpenShift AI and Kubeflow Trainer. It describes packaging distributed training in a TrainJob, scaling across nodes and GPUs, and using LoRA to keep memory usage below 16 GB. The reported result reduces speech errors by more than 90% compared with the base model. ### Source excerpt Can't Read, Won't Buy. That is the title CSA Research gave its survey of 8,709 consumers across 29 countries, and the numbers justify it: 76% prefer to buy in their own language, and 40% will never buy in another. The same rule governs what your product says out loud. The post Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI appeared first on Red Hat Developer. ## Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic DevFeed: [Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic]() Original publisher: [Read original article]() Author: Antonio Tiene; Alejo Lopez Avila; Iker García-Ferrero Published: 2026-09-08T14:23:07Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Chat]() Tags: [blog](), [deployment](), [experiments](), [hugging-face](), [llm](), [model](), [policy](), [safety](), [training]() ### AI overview The article examines LLM safety policies that refuse harmful requests within a topic while continuing to answer benign requests in that same topic. ### Source excerpt A Blog post by Multiverse Computing on Hugging Face ## CNCF Welcomes New Silver Members as Enterprises Scale AI From Training to Inference DevFeed: [CNCF Welcomes New Silver Members as Enterprises Scale AI From Training to Inference]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:58:47Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Cloud Native Ecosystem](), [Inference](), [AI Platform](), [Optimization](), [Kubernetes]() Tags: [ai](), [ai-infrastructure](), [announcements](), [cloud-native-ecosystem](), [inference](), [kubernetes](), [optimization](), [production](), [training]() ### AI overview CNCF announces nine new Silver members as enterprises shift AI workloads from training to production inference. The announcement emphasizes cloud-native infrastructure, operational efficiency, data sovereignty, and resource optimization. ### Source excerpt New members including SoftBank Corp. and Crusoe join the cloud native community to help build cost-efficient, sovereign infrastructure SHANGHAI, China - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 - September 8, 2026... ## OpenAI expands initiatives to support journalism from classrooms to newsrooms DevFeed: [OpenAI expands initiatives to support journalism from classrooms to newsrooms]() Original publisher: [Read original article]() Published: 2026-09-08T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI Chat]() Tags: [ai](), [chatgpt](), [company](), [generative-ai](), [media](), [news](), [openai](), [partnerships](), [support](), [tools](), [training]() ### AI overview OpenAI is expanding journalism support through training, partnerships, and ChatGPT Edu access for students and faculty at CUNY's Newmark J-School and Northwestern's Medill School. ### Source excerpt OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations. 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