# vertex Published articles for vertex. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Lyria 3 Pro: Create longer tracks in more DevFeed: [Lyria 3 Pro: Create longer tracks in more]() Original publisher: [Read original article]() Author: Myriam Hamed Torres Published: 2026-03-25T16:01:39Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Google AI](), [Artificial Intelligence](), [API](), [App]() Tags: [ai](), [api](), [app](), [audio](), [creative-tools](), [creativity](), [developers](), [experiment](), [gemini](), [generate](), [generation](), [google](), [google-ai](), [integrations](), [marketing](), [model](), [music](), [none](), [products](), [tool](), [vertex]() ### AI overview Lyria 3 Pro is Google's advanced music generation model for creating tracks up to three minutes long with greater customization and structural awareness. The announcement covers its availability across Vertex AI, Google AI Studio, the Gemini API, Google Vids, the Gemini app, and ProducerAI. ### Source excerpt Introducing Lyria 3 Pro, which unlocks longer tracks with structural awareness. We're also bringing Lyria to more Google products and surfaces. ## Gemini 3.1 Flash-Lite: Built for intelligence at scale DevFeed: [Gemini 3.1 Flash-Lite: Built for intelligence at scale]() Original publisher: [Read original article]() Author: Equipe do Google Gemini Published: 2026-03-03T16:35:55Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [API](), [Artificial Intelligence](), [Benchmark](), [multimodal](), [real-time](), [User Interfaces](), [dashboards]() Tags: [api](), [benchmark](), [benchmarks](), [cost](), [dashboards](), [efficiency](), [gemini](), [latency](), [models](), [multimodal](), [none](), [performance](), [real-time](), [reasoning](), [speed](), [user-interfaces](), [vertex]() ### AI overview Google introduces Gemini 3.1 Flash-Lite, a fast, cost-efficient model for high-volume developer workloads. Available in preview through the Gemini API, Google AI Studio, and Vertex AI, it emphasizes low latency, speed, quality, and configurable thinking levels for tasks ranging from translation and content moderation to interface and dashboard generation. ### Source excerpt Gemini 3.1 Flash-Lite is our fastest and most cost-efficient Gemini 3 series model yet. ## Gemini 3.1 Pro: A smarter model for your most complex tasks DevFeed: [Gemini 3.1 Pro: A smarter model for your most complex tasks]() Original publisher: [Read original article]() Author: Equipe do Google Gemini Published: 2026-02-19T16:06:14Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [API](), [Benchmark](), [Command-line interface](), [ide]() Tags: [ai](), [android-studio](), [api](), [benchmark](), [cli](), [developer](), [gemini](), [google](), [model](), [none](), [update](), [vertex]() ### AI overview Google DeepMind announces Gemini 3.1 Pro, an upgraded model for complex problem-solving and advanced reasoning. The preview is rolling out across developer, enterprise, and consumer products, including the Gemini API, Google AI Studio, Gemini CLI, Android Studio, Vertex AI, the Gemini app, and NotebookLM. ### Source excerpt 3.1 Pro is designed for tasks where a simple answer isn't enough. ## Introducing SyGra Studio DevFeed: [Introducing SyGra Studio]() Original publisher: [Read original article]() Author: Surajit Dasgupta; Bidyapati Pradhan; Amit Kumar Saha; Vipul Mittal; Sriram Puttagunta Published: 2026-02-05T16:52:28Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ai observability](), [debugging](), [dashboards]() Tags: [azure-openai](), [bedrock](), [cost](), [debugging](), [hugging-face](), [json](), [latency](), [llm](), [observability](), [openai](), [vertex](), [vllm](), [workflow]() ### AI overview SyGra Studio provides a guided interface for building and running LLM workflows. It connects data sources, configures models and structured outputs, and exposes execution status, costs, latency, guardrail outcomes, logs, and breakpoints. ### Source excerpt - Configure and validate models with guided forms (OpenAI, Azure OpenAI, Ollama, Vertex, Bedrock, vLLM, custom endpoints). - Connect Hugging Face, file-system, or ServiceNow data sources and preview rows before execution. - Configure nodes by selecting models, writing prompts (with auto-suggested variables), and defining outputs or structured schemas. - Design downstream outputs using shared state variables and Pydantic-powered mappings. ## How to use Vertex AI Prompt Optimizer with ground truth data DevFeed: [How to use Vertex AI Prompt Optimizer with ground truth data]() Original publisher: [Read original article]() Author: Alexander Nohe; Elena Erbiceanu Tener Published: 2026-01-20T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Prompt Engineering](), [Artificial Intelligence](), [Ground truth / benchmark quality](), [Google](), [CSV](), [data](), [Firebase]() Tags: [ai](), [cloud-storage](), [data](), [evaluation](), [firebase](), [format](), [gemini](), [google](), [google-cloud](), [how-to](), [learn](), [quality](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to use Vertex AI Prompt Optimizer with ground truth data to iteratively tune prompts and evaluate output quality. It describes preparing scripts and target descriptions in a Google Sheet, exporting the data as CSV, uploading it to Google Cloud Storage, and configuring optimization in a Colab Enterprise notebook. ### Source excerpt Learn how to use the Vertex AI Prompt Optimizer to automatically tune your prompts to get better results by iterating on your prompts and then running an evaluation on the outputs assessing their quality to see if it has improved. ## Veo 3.1 Ingredients to Video: More consistency, creativity and control DevFeed: [Veo 3.1 Ingredients to Video: More consistency, creativity and control]() Original publisher: [Read original article]() Author: Ricky Wong Published: 2026-01-13T17:00:18Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google](), [Google AI]() Tags: [ai](), [creativity](), [generation](), [images](), [none](), [updates](), [vertex](), [video]() ### AI overview Google announces updates to Veo 3.1 Ingredients to Video, adding more expressive generation from reference images, improved character and scene consistency, native vertical outputs, and upscaling to 1080p and 4K. The capabilities are launching across Gemini, YouTube, Flow, Google Vids, the Gemini API, and Vertex AI. ### Source excerpt Our latest Veo update generates lively, dynamic clips that feel natural and engaging -- and supports vertical video generation. ## Making Java a first-class AI citizen with Langchain4j DevFeed: [Making Java a first-class AI citizen with Langchain4j]() Original publisher: [Read original article]() Author: deors Published: 2025-12-18T03:03:33Z Content type: tutorial Language: en Sources: [Java Advent Calendar]() Topics: [Java](), [AI Development](), [Large Language Model](), [API](), [Resilience](), [Scalability](), [observability](), [OpenAI](), [Google AI](), [Ollama]() Tags: [2025](), [ai](), [ai-models](), [api](), [java](), [langchain4j](), [ollama](), [openai](), [vertex]() ### AI overview This article introduces LangChain4j as a framework-agnostic Java library for building and running generative AI solutions. It explains its API, integration with Spring, Jakarta, Quarkus, Micronaut, and standalone applications, and support for cloud and local models, with examples based on version 0.36.2. ### Source excerpt Introduction When the first technical solutions based on Artificial Intelligence began to be created, Python was the language and runtime platform of choice. It was not a total surprise as Python was the choice of the great majority of data scientists to analyse data, perform experiments and create AI models, so it was kind of [...] The post Making Java a first-class AI citizen with Langchain4j appeared first on JVM Advent. ## Building for an Open Future - our new partnership with Google Cloud DevFeed: [Building for an Open Future - our new partnership with Google Cloud]() Original publisher: [Read original article]() Author: Jeff Boudier; Simon Pagezy Published: 2025-11-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Cloud](), [Open Source](), [Cloud Run](), [inference-endpoints](), [Low-Latency Inference](), [Cache](), [xet](), [data](), [Deployment]() Tags: [announcement](), [artificial-intelligence](), [cache](), [cloud](), [data](), [datasets](), [deployment](), [google](), [google-cloud](), [inference-endpoints](), [networking](), [open](), [open-source](), [partnership](), [partnerships](), [storage](), [vertex](), [vertex-ai](), [xet]() ### AI overview Hugging Face and Google Cloud announce a strategic partnership focused on making open AI models easier to use, customize, deploy, and govern. The article describes integrations across Vertex AI, GKE AI/ML, Cloud Run GPUs, and other Google Cloud infrastructure, plus a planned CDN Gateway using Hugging Face Xet and Google Cloud storage and networking to accelerate model and dataset downloads and improve supply-chain robustness. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Automating Infrastructure as Code with Vertex AI DevFeed: [Automating Infrastructure as Code with Vertex AI]() Original publisher: [Read original article]() Published: 2024-11-05T22:23:09Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [Infrastructure as code](), [Artificial Intelligence](), [Generative AI](), [GitOps](), [Code](), [Google](), [Kubernetes]() Tags: [ai](), [apis](), [architecture](), [ci-cd](), [cloud](), [cloud-infrastructure](), [code](), [configuration](), [development](), [gcp](), [gemini](), [generative](), [generative-ai](), [iac](), [infrastructure](), [infrastructure-as-code](), [integration](), [konfigurate](), [llm](), [platform](), [product](), [use-cases](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to integrate Google Vertex AI with Gemini 1.5 into the Konfigurate platform to automate infrastructure-as-code creation. It covers multimodal input, context-aware and structured output, output tuning without model tuning, and model testing. ### Source excerpt A lot of companies are trying to figure out how AI can be used to improve their business. Most of them are struggling to not just implement AI, but to even find use cases that aren't contrived and actually add value to their customers. We recently discovered a compelling use case for AI integration in our Konfigurate platform, and we found that implementing generative AI doesn't require a great deal of complexity. I'm going to walk you through what we learned about integrating an AI assistant into our production system. There's a ton of noise out there about what you "need" to integrate AI into your product. The good news? You don't need much. The bad news? It took too much time sifting through nonsense to find what actually helps deliver value with AI. ## How We Built the BFCM 2023 Globe DevFeed: [How We Built the BFCM 2023 Globe]() Original publisher: [Read original article]() Author: Diego Macario Bello Published: 2024-10-30T19:03:30Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Three.js](), [Three.js custom shaders](), [Three.js shaders](), [real-time](), [3D](), [Shopify](), [data]() Tags: [3d](), [performance](), [react-three-fiber](), [real-time](), [render](), [shaders](), [shopify](), [three-js](), [vertex](), [visualization]() ### AI overview This article explains how Shopify built and optimized an interactive real-time 3D globe for BFCM purchases. It covers arcs representing orders, Bézier curves, mesh construction, camera-facing vertex-shader techniques, UV-based texturing, and independently animated dissolve effects using Three.js and React-three-fiber. ### Source excerpt Every year for Black Friday Cyber Monday (BFCM), we put together a real-time visualization of purchases made through Shopify-powered merchants worldwide. This year we're cooking up something big, and in anticipation we wanted to show you how we built the globe last year. Video of BFCM 2023 Globe Besides going for better visuals, a big focus was performance. ## Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI DevFeed: [Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI]() Original publisher: [Read original article]() Author: Alvaro Bartolome; Philipp Schmid; Simon Pagezy; Jeff Boudier Published: 2024-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [llama](), [Google Cloud Platform (GCP)](), [Inference](), [tgi](), [Meta](), [GPU](), [hugging face](), [Containers]() Tags: [container](), [cuda](), [gcp](), [google-cloud](), [gpu](), [hugging-face](), [inference](), [llama](), [nlp](), [partnerships](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to programmatically deploy the FP8-quantized Meta Llama 3.1 405B model on Google Cloud Vertex AI using Text Generation Inference and Hugging Face Deep Learning Containers. It covers deployment on an A3 node with eight NVIDIA H100 GPUs, alternative deployment paths, and the memory considerations for running the model. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing Vertex AI for Firebase DevFeed: [Introducing Vertex AI for Firebase]() Original publisher: [Read original article]() Author: Miguel Ramos; Rachel Saunders Published: 2024-05-14T03:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google AI](), [SDKs](), [API](), [Mobile](), [Web]() Tags: [ai](), [ai-logic](), [api](), [app-check](), [cloud-storage](), [firebase](), [firebase-remote-config](), [gemini](), [launch](), [machine-learning](), [mobile](), [remote-config](), [sdks](), [vertex](), [vertex-ai](), [web]() ### AI overview Google introduces Vertex AI for Firebase, providing client SDKs that let mobile and web applications use the Vertex AI Gemini API and run inference across Gemini models without requiring a separate backend service layer. ### Source excerpt Build AI features with the Vertex AI Gemini API using Firebase's new client SDKs ## AI Image Classification for PEZ Collectors | Vertex AI & MediaPipe on Android DevFeed: [AI Image Classification for PEZ Collectors | Vertex AI & MediaPipe on Android]() Original publisher: [Read original article]() Author: Mike Wolfson Published: 2024-03-26T18:00:00Z Content type: tutorial Language: en Sources: [My Big Appetite - Mike Wolfson]() Topics: [Artificial Intelligence](), [data](), [App](), [Mobile](), [Website](), [MediaPipe](), [Google]() Tags: [ai](), [android](), [app](), [classification](), [data](), [dataset](), [devices](), [google](), [image-classification](), [mediapipe](), [mobile](), [pez](), [tech](), [vertex](), [vertex-ai]() ### AI overview This tutorial describes building an image-classification model to distinguish visually similar PEZ dispensers. It covers creating a labeled photo dataset, training the model with Vertex AI, and using MediaPipe to support integration on smartphones or websites. ### Source excerpt I've always had a soft spot for PEZ dispensers and have been collecting them for over 30 years. These quirky little collectibles come in an incredible variety of shapes and characters. But there's more to PEZ than just fun; some dispensers can be quite valuable depending on their age and variation. To address the challenge of identification, I harnessed the power of AI to create an image classification model that could help identify these subtle differences. Use Case: Identifying PEZ dispensers Identifying the precise type of PEZ dispenser isn't always easy. Take Mickey Mouse dispensers, for example. While even a novice collector can broadly identify it as Mickey, subtle differences in the face or eyes could separate a common version from a prized rarity. A $1 Mickey and a $150 Mickey can look awfully similar. To illustrate the problem, these Mickey Mouse dispensers look similar, but are worth drastically different amounts (from left-to-right). Mickey Die-Cut (1961) - $125 Mickey B (1989) - $15 Mickey C (1997) - $1 It is fairly easy for even an untrained human to tell the difference between these dispensers. There are obvious differences between the shape of the face, and the eyes. Could a Computer Tell the Difference? I decided to see if I could train a custom image classification model to distinguish between PEZ dispensers. That model, in theory, could be embedded into a mobile app or a website, giving PEZ enthusiasts a tool to aid in identification. For this project, I embraced technologies from Google: Vertex AI to manage my image dataset and train the model, and MediaPipe for easy integration onto edge devices like smartphones. Step 1: Picture This - Building the Dataset Like any good AI project, I needed data. My original plan was to scrape images from the web, but there simply weren't enough consistent, high-quality pictures for the level of detail I wanted. The best solution? Become a PEZ photographer! I gathered a dozen dispensers from my collection and met ## Binary Search on Graphs DevFeed: [Binary Search on Graphs]() Original publisher: [Read original article]() Published: 2017-11-08T08:59:38Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms, Complexity](), [Graphs](), [Algorithm]() Tags: [algorithm](), [algorithms](), [binary-search](), [depth-first-search](), [dijkstra](), [equivalence-queries](), [graph](), [graphs](), [learning-theory](), [mathematics](), [programming](), [python](), [search](), [vertex]() ### AI overview The article examines whether binary search can be applied to graphs. It presents a graph-search model in which queries about vertices return either the target or an edge on a shortest path toward it, and notes that the line-graph case corresponds to ordinary binary search. ### Source excerpt Binary search is one of the most basic algorithms I know. Given a sorted list of comparable items and a target item being sought, binary search looks at the middle of the list, and compares it to the target. If the target is larger, we repeat on the smaller half of the list, and vice versa. With each comparison the binary search algorithm cuts the search space in half. The result is a guarantee of no more than $ \log(n)$ comparisons, for a total runtime of $ O(\log n)$. ## A Spectral Analysis of Moore Graphs DevFeed: [A Spectral Analysis of Moore Graphs]() Original publisher: [Read original article]() Published: 2016-11-03T08:00:14Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Graphs]() Tags: [adjacency-matrix](), [eigenvalues](), [eigenvectors](), [graph](), [graphs](), [math](), [matrix](), [moore-graph](), [orthogonality](), [regular](), [spectral-graph-theory](), [trace](), [vertex]() ### AI overview This mathematical article analyzes Moore graphs of girth 5 using the eigenvalues of their adjacency matrices. It derives the minimum vertex count and shows that the degree must be one of 3, 7, or 57. ### Source excerpt For fixed integers $ r > 0$, and odd $ g$, a Moore graph is an $ r$-regular graph of girth $ g$ which has the minimum number of vertices $ n$ among all such graphs with the same regularity and girth. (Recall, A the girth of a graph is the length of its shortest cycle, and it's regular if all its vertices have the same degree) Problem (Hoffman-Singleton): Find a useful constraint on the relationship between $ n$ and $ r$ for Moore graphs of girth $ 5$ and degree $ r$. ## Zero-One Laws for Random Graphs DevFeed: [Zero-One Laws for Random Graphs]() Original publisher: [Read original article]() Published: 2015-02-09T09:00:00Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Graphs](), [Math and Logic]() Tags: [big-o-notation](), [connectivity](), [countability](), [distribution](), [erdos-renyi](), [graphs](), [logic](), [logical](), [mathematics](), [model-theory](), [network-science](), [parameter](), [random-graph](), [random-graphs](), [statement](), [vertex]() ### AI overview This article introduces zero-one laws for Erdős-Rényi random graphs. It explains that many graph properties, including properties expressible in first-order logic, have probabilities that tend toward zero or one as the graph grows, with behavior determined by relevant thresholds or constant edge probabilities. ### Source excerpt Last time we saw a number of properties of graphs, such as connectivity, where the probability that an Erdős-Rényi random graph $ G(n,p)$ satisfies the property is asymptotically either zero or one. And this zero or one depends on whether the parameter $ p$ is above or below a universal threshold (that depends only on $ n$ and the property in question). To remind the reader, the Erdős-Rényi random "graph" $ G(n,p)$ is a distribution over graphs that you draw from by including each edge independently with probability $ p$. ## When Greedy Algorithms are Perfect: the Matroid DevFeed: [When Greedy Algorithms are Perfect: the Matroid]() Original publisher: [Read original article]() Published: 2014-08-26T09:00:02Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Algorithms, Complexity](), [graph theory](), [Graphs](), [math]() Tags: [algorithms](), [graph-theory](), [graphs](), [greedy](), [greedy-algorithm](), [kruskal-s-algorithm](), [linear-independence](), [matroids](), [minimum-spanning-trees](), [trees](), [vertex]() ### AI overview This article explains when greedy algorithms are guaranteed to produce optimal solutions. It introduces matroids as the framework characterizing that guarantee and uses the minimum spanning tree problem as an example, with background on matroid history and connections to linear algebra and graph theory. ### Source excerpt Greedy algorithms are by far one of the easiest and most well-understood algorithmic techniques. There is a wealth of variations, but at its core the greedy algorithm optimizes something using the natural rule, "pick what looks best" at any step. So a greedy routing algorithm would say to a routing problem: "You want to visit all these locations with minimum travel time? Let's start by going to the closest one. And from there to the next closest one. ## n-Colorability is Equivalent to Finite n-Colorability (A Formal Logic Proof) DevFeed: [n-Colorability is Equivalent to Finite n-Colorability (A Formal Logic Proof)]() Original publisher: [Read original article]() Published: 2011-09-04T22:41:27Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Graphs](), [graph theory](), [Math and Logic]() Tags: [completeness](), [graph](), [graph-coloring](), [graph-theory](), [logic](), [vertex]() ### AI overview A formal logic proof shows that an infinite graph is n-colorable exactly when every finite subgraph is n-colorable. The proof encodes valid colorings as propositional formulas and applies the Compactness Theorem. ### Source excerpt Warning: this proof requires a bit of familiarity with the terminology of propositional logic and graph theory. Problem: Let $ G$ be an infinite graph. Show that $ G$ is $ n$-colorable if and only if every finite subgraph $ G_0 \subset G$ is $ n$-colorable. Solution: One of the many equivalent versions of the Compactness Theorem for the propositional calculus states that if $ \Sigma \subset \textup{Prop}(A)$, where $ A$ is a set of propositional atoms, then $ \Sigma$ is satisfiable if and only if any finite subset of $ \Sigma$ is satisfiable. ## Google's PageRank--A First Attempt DevFeed: [Google's PageRank--A First Attempt]() Original publisher: [Read original article]() Published: 2011-06-18T18:05:20Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Graphs](), [graph theory](), [Web](), [Algorithm](), [Internet](), [structure]() Tags: [algorithm](), [analysis](), [eigenvalues](), [eigenvectors](), [google](), [graph](), [graph-theory](), [internet](), [mathematics](), [page-rank](), [ranking](), [search-engine](), [structure](), [vertex](), [web]() ### AI overview This post models the Web as a directed graph and introduces PageRank-style importance scoring for web pages. It first considers ranking pages by incoming-link counts, then explains why treating every link as equally valuable is inadequate and motivates weighting links by the importance of the linking page. ### Source excerpt The Web as a Graph The goal of this post is to assign an "importance score" $ x_i \in [0,1]$ to each of a set of web pages indexed $ v_i$ in a way that consistently captures our idea of which websites are likely to be important. But before we can extract information from the structure of the internet, we need to have a mathematical description of that structure. Enter graph theory.