# multimodal Multimodal generation refers to models that process diverse input types and generate outputs in different modalities. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error DevFeed: [NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:43:16Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [lunar foundation model](), [foundation-models](), [ibm](), [Open Source](), [hugging face](), [dataset](), [multimodal](), [Benchmark]() Tags: [ai](), [benchmark](), [data](), [enterprise](), [fine-tuning](), [foundation-models](), [hugging-face](), [ibm](), [lunar-foundation-model](), [machine-learning](), [multimodal](), [nasa](), [open-source](), [space]() ### AI overview IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source on Hugging Face, along with its weights, technical report, and training dataset. Built on TerraMind, the model uses multimodal lunar observations for tasks including ice-deposit mapping, volcanic-feature detection, and crater detection. Reported benchmarks show up to 22% lower ice-mapping error than SwinV2-B, while the accompanying dataset contains roughly 2 million image tiles from nine instruments across four lunar missions. ### Source excerpt IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, The post NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error appeared first on StorageReview.com. ## 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... ## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-09T20:31:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Optimization](), [model-deployment](), [multimodal](), [Dynamo](), [Inference Performance](), [Embeddings](), [Mixture of Experts (MoE)](), [GPU](), [Open Source](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [computer-vision-video-analytics](), [developer-tools-techniques](), [dynamo](), [embeddings](), [gpu](), [inference](), [low-latency-inference](), [mixture-of-experts-moe](), [model-serving](), [multimodal](), [nvfp4](), [open-source](), [optimization]() ### AI overview This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time. ### Source excerpt Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... ## From voice command to robotic arm: how agentic AI on the edge is changing the factory floor DevFeed: [From voice command to robotic arm: how agentic AI on the edge is changing the factory floor]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-01T12:20:24Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Artificial Intelligence](), [foundation-models](), [Automation](), [Arduino](), [UNO Q](), [AI Agent](), [Physical AI](), [multimodal]() Tags: [agentic-ai](), [ai](), [arduino](), [automation](), [featured](), [foundation-models](), [industrial](), [inference](), [latency](), [manufacturing](), [multimodal](), [robotic-arm](), [robotics](), [smart-factory](), [uno-q](), [usb](), [voice-commands](), [voice-control]() ### AI overview The article describes a demonstration in which Forgis uses a foundation model running on an Arduino UNO Q board to convert voice commands into robotic-arm actions. The system processes multimodal factory data and performs inference locally, enabling real-time control without a cloud round trip. ### Source excerpt For years, bringing real intelligence to industrial automation meant expensive infrastructure, proprietary systems, and steep learning curves. That's changing - fast. Foundation models powerful enough to run at the edge are turning natural language into machine control, and the factory floor is starting to look a lot more like a conversation. AI as the new [...] The post From voice command to robotic arm: how agentic AI on the edge is changing the factory floor appeared first on Arduino Blog. ## MAPS: Netflix's Multimodal Asset Personalization at Scale DevFeed: [MAPS: Netflix's Multimodal Asset Personalization at Scale]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-08-28T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [personalization](), [Embeddings](), [multimodal](), [recommendation systems](), [data](), [A/B Testing]() Tags: [data](), [embedding](), [embeddings](), [machine-learning](), [model](), [multimodal](), [netflix](), [personalization](), [production](), [testing]() ### AI overview Netflix describes MAPS, a multimodal asset-personalization system that uses embeddings from artwork and video previews to address cold-start problems and personalize recommendations soon after a title launches. ### Source excerpt By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray Zhang Introduction The Netflix experience is a journey of discovery. Every visual cue, from the artwork on a title to the video previews that autoplay while you browse, is there to connect you with a story you will love. We call these visual cues assets, and choosing the right one for each member is a personalization problem of its own. But which image or video preview of Squid Game should we show you? And what do we do right after a title launches, when there's far too little interaction data to know which asset we should recommend to each member? For years, our models answered the first question well and the second poorly. They learned which assets members interacted with, but treated every asset as an opaque ID, blind to what was actually in the artwork or video preview. Right after a title launched, its assets had no history, so we dialed up exploration on its assets to gather interaction data, and otherwise fell back to popularity heuristics that ignore your taste. Only once enough interactions had piled up could personalization take over. This is the classic cold-start problem. This post shares how multimodal embeddings let our models see and hear the assets they recommend, so personalization can kick in far sooner, close to a title's launch. Because a new asset arrives with its embedding the model already understands, that embedding carries member taste signals from related assets immediately. Consequently, the model needs far less interaction history before it can personalize. We cover three production systems, artwork personalization, query-aware artwork ranking, and video preview personalization, plus a cheap trick for choosing new embeddings before committing to full end-to-end integration and A/B testing. Artwork Personalization A single image is often a member's first touchpoint with a title, so we create a diverse set of artworks for each tit ## Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding DevFeed: [Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-26T17:07:12Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [qwen](), [long-context](), [Mixture of Experts (MoE)](), [agentic-coding](), [Inference](), [GPU](), [multimodal](), [NeMo](), [sglang](), [TensorRT-LLM](), [vllm](), [rust-ai]() Tags: [agentic-ai-generative-ai](), [agentic-coding](), [benchmarks](), [context-window](), [developer-tools-techniques](), [gb300-nvl72](), [inference](), [mixture-of-experts-moe](), [multimodal](), [nemo](), [nvlink](), [qwen](), [sglang](), [tensorrt-llm](), [top-stories](), [vllm]() ### AI overview This NVIDIA developer article introduces Qwen3.8-Flash-Next, a multimodal mixture-of-experts model released by Alibaba for experimentation and evaluation. It explains the model's long-context hybrid architecture, including Gated DeltaNet and Qwen Sparse Attention, and discusses reported efficiency improvements for million-token workloads. The article also covers inference support through SGLang, vLLM, TensorRT-LLM, and NVIDIA NeMo, plus performance on the NVIDIA GB300 NVL72 platform. ### Source excerpt Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate. It's... ## Elastic community newsletter -- August 2026 DevFeed: [Elastic community newsletter -- August 2026]() Original publisher: [Read original article]() Author: Elastic DevRel team Published: 2026-08-13T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [devrel](), [dashboards](), [Embeddings](), [multimodal](), [quantization](), [AI search](), [tracing]() Tags: [audio](), [dashboards](), [devrel](), [elasticsearch](), [embeddings](), [images](), [multimodal](), [pdf](), [quantization](), [tracing](), [video]() ### AI overview The Elastic community newsletter covers Elasticsearch 9.5, including native PromQL, the Dashboards API, Columnar Mode, vector search improvements, multimodal semantic search, and Elastic Agent Builder tracing in technical preview. ### Source excerpt In this newsletter, we cover version 9.5 of Elasticsearch, the latest blogs and videos, and upcoming events. ## Meta is back with Muse Glimmer: local, agentic, multimodal, and open source DevFeed: [Meta is back with Muse Glimmer: local, agentic, multimodal, and open source]() Original publisher: [Read original article]() Author: Pedro Cuenca; merve; ben burtenshaw; Aritra Roy Gosthipaty Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [multimodal](), [Artificial Intelligence](), [hugging face](), [Benchmark](), [coding]() Tags: [agentic](), [artificial-intelligence](), [benchmark](), [hub](), [hugging-face](), [images](), [llama-cpp](), [llms](), [local](), [meta](), [model](), [multimodal](), [muse](), [open-source](), [videos](), [vllm](), [vlm](), [vlms]() ### AI overview Hugging Face presents Muse Glimmer, a local, agentic, multimodal, open-source 30B-parameter vision-language model developed with Meta. The article outlines its vision and language architecture, benchmark context, optional speculative decoding for faster generation, and support for both images and videos. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Gemini Robotics 2 brings whole body intelligence to robots DevFeed: [Gemini Robotics 2 brings whole body intelligence to robots]() Original publisher: [Read original article]() Author: Carolina Parada Published: 2026-07-28T13:21:37Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Robotics](), [AI Models](), [Artificial Intelligence](), [vlm](), [multimodal]() Tags: [agent](), [ai](), [ai-models](), [collaboration](), [gemini](), [models](), [multimodal](), [on-device](), [robotics](), [robots](), [teamwork](), [vision]() ### AI overview Gemini Robotics 2 is presented as an intelligence layer for adaptable robots, enabling whole-body control, dexterous manipulation, multi-robot teamwork, and adaptation to new robotic bodies. The article describes three models: a vision-language-action model for motor control, an embodied reasoning vision-language model for communication and multi-step planning, and an on-device model optimized for local operation. ### Source excerpt From feet to fingertips -- we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks. ## Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber DevFeed: [Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber]() Original publisher: [Read original article]() Author: Tulsee Doshi Published: 2026-07-21T15:16:30Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [releases](), [Latency](), [multimodal](), [Security](), [Cybersecurity]() Tags: [agentic](), [ai](), [ai-agents](), [code-security](), [cost](), [cybersecurity](), [gemini](), [latency](), [models](), [multimodal](), [none](), [performance](), [reasoning](), [releases](), [tokens]() ### AI overview Google DeepMind introduces Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. The release focuses on more efficient production AI agents through lower latency, reduced token usage, lower costs, and improved coding, knowledge-work, multimodal, and cybersecurity performance. ### Source excerpt We're introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber. ## Welcome Inkling by Thinking Machines DevFeed: [Welcome Inkling by Thinking Machines]() Original publisher: [Read original article]() Author: ben burtenshaw; merve; Pedro Cuenca; Aritra Roy Gosthipaty; Andres Marafioti Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Mixture of Experts (MoE)](), [multimodal](), [hugging face](), [Inference](), [Deployment](), [Fine-tuning](), [Open Source](), [sglang]() Tags: [announcement](), [artificial-intelligence](), [audio](), [context-window](), [deployment](), [fine-tuning](), [generation](), [inference](), [inference-endpoints](), [llama-cpp](), [llms](), [mixture-of-experts](), [mixture-of-experts-moe](), [moe](), [multimodal](), [open-source](), [sglang](), [transformers](), [vllm]() ### AI overview Thinking Machines Lab's Inkling is presented as a large open multimodal language model that accepts image, text, and audio inputs. The article covers its mixture-of-experts architecture, million-token context window, reasoning across modalities, fine-tuning use cases, model variants, and deployment through Hugging Face Inference Endpoints and inference frameworks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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 ## Muse Spark 1.1 is now available on AI Gateway DevFeed: [Muse Spark 1.1 is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [multimodal](), [long-context](), [Model Context Protocol](), [vercel ai sdk](), [API keys](), [Routing (disambiguation)]() Tags: [agent](), [agentic](), [ai](), [audio](), [context-window](), [image](), [leaderboard](), [model](), [multimodal](), [muse](), [pricing](), [ranking](), [routing](), [sdk](), [spark](), [tokens](), [tool](), [tools]() ### AI overview Muse Spark 1.1 from Meta is now available through Vercel's AI Gateway. It is a multimodal reasoning model with a 1M-token context window for agentic tasks, supporting multiple input types, tool orchestration, MCP servers, custom skills, parallel tool calls, structured output, and search with citations. ### Source excerpt Muse Spark 1.1 from Meta is now available on AI Gateway. It is a multimodal reasoning model with a 1M token context window built for agentic tasks, accepting text, image, video, PDF, and audio inputs. Muse Spark 1.1 plans and orchestrates work across tools and services, operating as a main agent or as a subagent, and it works with new tools, MCP servers, and custom skills without examples. The model supports parallel tool calling, structured output, and built-in search with citations. To use Muse Spark 1.1, set model to meta/muse-spark-1.1 in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Muse Spark 1.1 in the model playground. Read more ## Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T DevFeed: [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-07T17:05:42Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [Model Development](), [Open Source](), [Training AI Models](), [datasets](), [multimodal](), [Deployment](), [Simulation and Design](), [Tooling]() Tags: [ai-foundation-models](), [apache](), [building](), [data](), [deployment](), [developers](), [development](), [evaluation](), [featured](), [humanoid-robots](), [images](), [integrations](), [isaac](), [isaac-sim](), [manufacturing](), [model](), [multimodal](), [nvidia](), [open-source](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [software](), [tooling](), [training](), [workflows]() ### AI overview NVIDIA Isaac GR00T Development Platform unifies humanoid robot development workflows, combining data collection, simulation-based training, evaluation, and deployment. The article highlights the open Isaac GR00T 1.7 vision-language-action model, which accepts language and images and can be adapted to robots, tasks, and environments through post-training. ### Source excerpt As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids... ## Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) now on AI Gateway DevFeed: [Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) now on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [vercel ai sdk](), [Google](), [multimodal](), [AI Models](), [API]() Tags: [ai](), [ai-gateway](), [ai-models](), [gemini](), [image](), [inference](), [multimodal](), [pricing]() ### AI overview Vercel AI Gateway now offers Google's Nano Banana 2 Lite, a multimodal image model for fast, lower-cost image generation and multi-turn image editing. It supports generating images alongside text through the AI SDK and is available in the model playground. ### Source excerpt Nano Banana 2 Lite from Google is now available on AI Gateway. This Flash-Lite-tier image model is built for fast, low-cost generation. It generates images alongside text in <4s and can edit existing images across multiple turns. The cost is also lower than previous Nano Banana models. Nano Banana 2 Lite generates 1K images at $0.034 each, about half the cost of Nano Banana 2 and roughly a quarter of the cost of Nano Banana Pro at the same resolution. This model is multimodal. Use streamText or generateText to generate images alongside text responses. To use Nano Banana 2 Lite, set model to google/gemini-3.1-flash-lite-image in the AI SDK: Here is the example output from the above prompt: You can also try Nano Banana 2 Lite in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Read more ## Groq on Endless Compute, Inside Claude's Mind, and GLM-5.2 Open Weights - The Tokenizer Edition #32 DevFeed: [Groq on Endless Compute, Inside Claude's Mind, and GLM-5.2 Open Weights - The Tokenizer Edition #32]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-06-21T16:45:54Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Large Language Model](), [Mixture of Experts (MoE)](), [Inference](), [multimodal](), [groq](), [Claude]() Tags: [ai](), [ai-ml](), [github](), [inference](), [llm](), [mixture-of-experts](), [multimodal]() ### AI overview The Tokenizer Edition #32 curates AI and machine learning resources covering model interpretability, compute demand, open-weight models, multimodal video processing, speculative decoding, reinforcement learning, agent testing, and tools for cheaper or safer inference. ### Source excerpt This week's most valuable AI resources ## Kimi K2.7 Code now available on AI Gateway DevFeed: [Kimi K2.7 Code now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-06-12T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Code](), [API](), [SDKs](), [DevOps](), [Front end](), [multimodal](), [Optimization](), [Claude]() Tags: [ai](), [ai-gateway](), [api](), [api-keys](), [code](), [cost](), [devops](), [frontend](), [inference](), [multimodal](), [optimization](), [platform](), [pricing](), [sdk]() ### AI overview Vercel announces that Moonshot AI's Kimi K2.7 Code is available through AI Gateway. The coding model is designed for long-horizon programming tasks, supports text and vision input through a native multimodal architecture, and runs in thinking mode. AI Gateway provides a unified API for model access, usage and cost tracking, retries, failover, reporting, API key budgets, and related operational controls. ### Source excerpt Kimi K2.7 Code from Moonshot AI is now available on AI Gateway. K2.7 Code is a coding model built for long-horizon programming tasks, generalizing across scenarios including frontend development, DevOps, and performance optimization. The model has a native multimodal architecture that supports text and vision input, and always runs in thinking mode. To use K2.7 Code, set model to moonshotai/kimi-k2.7-code in the AI SDK: Pass an image alongside a prompt to use the model's multimodal input: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## Jonathan Frankle on Infrastructure for Specifying and Evaluating Frontier AI Systems DevFeed: [Jonathan Frankle on Infrastructure for Specifying and Evaluating Frontier AI Systems]() Original publisher: [Read original article]() Author: UX Magazine Team Published: 2026-06-11T10:46:59Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [Artificial Intelligence](), [long-context](), [large-language-models](), [databricks](), [LLM evaluation / benchmarking](), [systems](), [User experience (UX)](), [multimodal](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [benchmarks](), [claude](), [context](), [databricks](), [eval](), [language-models](), [large-language-models](), [multimodal](), [systems]() ### AI overview Jonathan Frankle argues that frontier language models have advanced faster than the infrastructure needed to specify, test, and deploy them reliably. The discussion emphasizes requirements, evaluation, composable tools, context strategies, retrieval, and prompts as parameters. ### Source excerpt Jonathan Frankle on why frontier models outran our ability to specify, evaluate, and curate, and what that means for design leaders shipping agentic systems. The metaphor lands because it names the embarrassment. On his return to Invisible Machines, a conversation recorded last summer, Jonathan Frankle, Chief AI Scientist at Databricks and head of Mosaic Research, The post Nuclear Fusion, No Power Lines appeared first on UX Magazine. ## Introducing Gemma 4 12B: a unified, encoder-free multimodal model DevFeed: [Introducing Gemma 4 12B: a unified, encoder-free multimodal model]() Original publisher: [Read original article]() Author: Olivier Lacombe Published: 2026-06-09T14:10:19Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [gemma4](), [multimodal](), [Language models](), [moe](), [Hardware](), [Latency]() Tags: [apache](), [community](), [gemma](), [laptop](), [latency](), [llm](), [mixture-of-experts-moe](), [multimodal](), [none]() ### AI overview Gemma 4 12B is an encoder-free multimodal model designed to deliver agentic intelligence locally on laptops. It combines native vision and audio processing, near-26B benchmark performance, a reduced memory footprint, and Apache 2.0 licensing. ### Source excerpt An overview of Gemma 4 12B, a model designed to bring high-performance multimodal intelligence directly to your laptop. ## Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI DevFeed: [Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI]() Original publisher: [Read original article]() Author: Varun Singh; Isabel Hulseman; Anuj Doshi; Shyamala Prayaga Published: 2026-06-04T18:57:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Nemotron](), [multimodal](), [Artificial Intelligence](), [Open Source Models & Datasets](), [long-context]() Tags: [ai](), [context-window](), [data](), [multimodal](), [nemotron](), [production]() ### AI overview Nemotron 3.5 Content Safety adds deeper multimodal safety analysis by evaluating a user prompt, optional image, and optional assistant response together in one context window. It supports custom policy specifications, multilingual transfer, optional reasoning traces, low-latency verdicts, and the release of its safety dataset for production safety pipelines. ### Source excerpt This post covers what changes in 3.5, the design decisions behind each new capability, and how to integrate the model into production safety pipelines. Nemotron 3 introduced image understanding; Nemotron 3.5 deepens the multimodal integration. The model takes a user prompt, an optional image, and an optional assistant response as a single context window and produces a coherent safety verdict over the combined input. ## Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale DevFeed: [Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale]() Original publisher: [Read original article]() Author: Pablo Seibelt Published: 2026-06-04T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [gemma4](), [llama.cpp](), [Local AI](), [Infrastructure as code](), [macOS](), [On-device AI](), [multimodal]() Tags: [ai](), [download](), [gemma-4](), [gemma4](), [gpu](), [infrastructure-as-code](), [kubernetes](), [llama-cpp](), [macos](), [multimodal](), [python](), [tailscale](), [tutorials]() ### AI overview A tutorial for self-hosting Gemma 4 12 B on a modern Mac using llama.cpp with Apple Metal acceleration. It combines host-native inference with a local Kubernetes cluster, Pulumi infrastructure as code, and Tailscale for secure access, and reports validation results on a MacBook Pro with an Apple M3 Max and 36 GB RAM. ### Source excerpt If you run AI tools and agents, you've probably accepted three tradeoffs: your data leaves your network, you can't work offline, and your bill scales with usage. Open-weight models now run well on consumer hardware. Once the model is on your machine, your data stays local, inference works offline, and tokens cost nothing. If you own a modern Mac, you can run a high-quality model yourself. Gemma 4 is an open-weights model family from Google. This post focuses on Gemma 4 12 B, released in June 2026, using Unsloth's Q8_0 GGUF. The 12 B model fits comfortably on a modern Mac while leaving enough headroom for local llama.cpp and a chat UI. We'll use llama.cpp for host-native inference, k3d for a local Kubernetes cluster, Pulumi for infrastructure as code, and Tailscale for secure access. Prerequisites This setup was validated on the following hardware: macOS 26 Tahoe, version 26.5 MacBook Pro with Apple M3 Max 36 GB RAM On this machine, llama.cpp reported about 20 output tokens per second for a 160-token validation response with unsloth/gemma-4-12b-it-GGUF, gemma-4-12b-it-Q8_0.gguf, and a 131,072-token context. Sustained throughput varies by prompt length, thermal state, and llama.cpp settings. You'll need brew, docker, pulumi, and tailscale installed. We'll also install k3d during the process. Run Gemma 4 with host-native llama.cpp We use llama.cpp directly on macOS to leverage Apple Metal acceleration. Running the LLM on the host is more efficient than trying to pass GPU access into a local Kubernetes VM. Install the build tools: brew install cmake git Then build llama.cpp from source and download the multimodal projector. In validation, Homebrew llama.cpp 9430 could run text inference, but it could not load the new Gemma 4 12 B projector and failed with unknown projector type: gemma4uv. Building current llama.cpp from source fixed that. llm_home="$HOME/pulumi-gemma4-llm" mkdir -p "$llm_home/models" "$llm_home/logs" if [ ! -d "$llm_home/llama.cpp/.git" ]; then git clon ## Introducing new capabilities to GPT-Rosalind DevFeed: [Introducing new capabilities to GPT-Rosalind]() Original publisher: [Read original article]() Published: 2026-06-03T13:15:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Benchmark](), [multimodal](), [Optimization](), [coding](), [data]() Tags: [analysis](), [benchmark](), [benchmarks](), [chemistry](), [coding](), [data](), [drug-discovery](), [gpt](), [life](), [model](), [models](), [multimodal](), [optimization](), [product](), [reasoning](), [research](), [validation]() ### AI overview GPT-Rosalind is a specialized model for life sciences research that combines agentic coding and tool use with capabilities in medicinal chemistry, genomics, biological reasoning, and experimental workflows. The article describes its global availability to eligible organizations, performance evaluations, the LifeSciBench benchmark, and MedChemBench's assessment of realistic medicinal chemistry tasks. ### Source excerpt GPT-Rosalind advances life sciences research with enhanced biological reasoning, medicinal chemistry expertise, genomics analysis, and experimental workflow capabilities. ## MiniMax M3 on AI Gateway DevFeed: [MiniMax M3 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-05-31T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [vercel ai sdk](), [Vercel](), [context window](), [multimodal](), [Software Engineering](), [Terminal](), [Latency]() Tags: [agentic](), [ai](), [ai-gateway](), [context-window](), [cost](), [inference](), [latency](), [multimodal](), [software-engineering](), [terminal](), [vercel](), [web]() ### AI overview Vercel AI Gateway now supports MiniMax M3, a multimodal model with a 1M-token context window, improved software engineering and terminal-based tool use, agentic web browsing, and multi-turn collaboration. The AI Gateway provides unified model access, usage and cost tracking, reliability features, provider sorting, reporting, Zero Data Retention support, and no markup or platform fee on inference. ### Source excerpt MiniMax M3 is now available on Vercel AI Gateway. M3 is MiniMax's first model with a 1M-token context window and native multimodality, built around MiniMax Sparse Attention (MSA). M3 improves on software engineering, terminal-based tool use, and agentic web browsing, and is tuned for multi-turn collaboration. To use MiniMax M3, set model to minimax/minimax-m3 in the AI SDK. Pass an image alongside a prompt to use M3's multimodal input: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, dynamic provider sorting by latency & cost, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## AI Now Summit 2026 DevFeed: [AI Now Summit 2026]() Original publisher: [Read original article]() Published: 2026-05-28T12:00:20Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Security](), [datacenter](), [multimodal](), [data](), [Robotics](), [Flight]() Tags: [agentic-ai](), [ai](), [automotive](), [data-center](), [design](), [industry](), [innovation](), [multimodal](), [partner](), [partnership](), [physics](), [production](), [robotics](), [security]() ### AI overview Mistral presents an AI stack for industrial engineering, combining physics models, engineering expertise, and robotics to support design, simulation, and production. Partnerships with Airbus, BMW, and ASML target aircraft operations, crash simulation, and semiconductor engineering, with an emphasis on proprietary-data security. The company also describes Vibe as a unified agent for long-running coding and research tasks and announces a new 10 MW data center in France. ### Source excerpt Innovations for global enterprises solving the world's hardest problems. [Next page]()