# Explore Published articles for Explore. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Introduction to pandas DevFeed: [Quiz: Introduction to pandas]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-18T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [pandas](), [CSV](), [data](), [Data Science](), [Statistics](), [Web](), [save]() Tags: [csv](), [data](), [explore](), [file](), [how-to](), [interactive](), [introduction](), [pandas](), [save](), [statistics](), [table](), [tables](), [test](), [web]() ### AI overview An interactive seven-question quiz tests core pandas skills, including reading tables from web pages, saving data as CSV, understanding DataFrames, chaining methods, and calculating statistics. ### Source excerpt Test your pandas basics: read tables from the web, save data to CSV, explore a DataFrame, and calculate averages in this interactive quiz. ## Grafana 13.2 release: easier ways to query and explore your data DevFeed: [Grafana 13.2 release: easier ways to query and explore your data]() Original publisher: [Read original article]() Author: Grafana Labs Team Published: 2026-09-12T11:22:06.456390Z Content type: release Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [dashboards]() Tags: [analytics](), [dashboards](), [enterprise](), [explore](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [release](), [sql](), [traces]() ### AI overview Grafana 13.2 introduces generally available saved queries for Grafana Cloud and Grafana Enterprise, letting organizations store, discover, and reuse vetted queries across dashboards, Explore, and annotation queries. The release also highlights a new View panel sidebar for exploring busy panels. ### Source excerpt Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. In this post, we'll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We'll also explore how the new View panel sidebar makes exploring busy panels a breeze. If you want to read about all the latest updates in Grafana 13.2, please refer to the changelog or our What's New documentation. Saved queries: reuse trusted queries across dashboards and teams Good queries are hard-won. Writing one means knowing both the query language and your own data, like which of four similarly named metrics is the one you can trust. That knowledge usually sits with a few experienced people, or is gradually learned through exploration (increasingly AI-assisted), validation, and revision. Often teams end up rebuilding the same Grafana queries over and over, and the best ones live in pinned Slack messages or get copy-pasted from old dashboards. New team members feel it most, since their first weeks are often spent reverse-engineering existing dashboards just to work out how to ask a question of their own. The query history in Grafana Explore helps, keeping a couple of weeks of your own queries and letting you "star" the keepers. It's private to you, though. Until recently, there hasn't been a built-in way to take a query you trust and put it somewhere your whole organization can find it. How teams use saved queries We built saved queries, which is now generally available in Grafana Cloud and Grafana Enterprise, to address this challenge by providing a shared query library for your organization. When you write a query worth keeping, you can save it with a title, description, and tags. Saving works from dashboard panels, Explore, and annotation queries. This means teammates who don't know PromQL or SQL can still build dashboards from queries tha ## VMware Explore 2026 in Las Vegas | Day 4 Recap DevFeed: [VMware Explore 2026 in Las Vegas | Day 4 Recap]() Original publisher: [Read original article]() Author: ashley woodbury Published: 2026-09-03T19:00:00Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [vcf 9.1](), [cloud-infrastructure](), [releases]() Tags: [2026](), [announcements](), [company](), [event](), [explore](), [explore-2026-in-las-vegas](), [news](), [recap](), [vcf-9-1](), [video-library](), [vmware](), [vmware-cloud-foundation](), [vmware-explore-2026-in-las-vegas]() ### AI overview A recap of VMware Explore 2026 in Las Vegas, covering learning sessions, hands-on labs, expert speakers, certifications, product releases, and VMware Private AI with VMware Cloud Foundation (VCF) 9.1. The event attracted more than 4,700 attendees across sessions focused on cloud infrastructure, app modernization, security, and innovation. ### Source excerpt VMware Explore 2026 in Las Vegas Day 4 Recap | September 3, 2026 Focused Learning. Immediate Impact. This past week was jam-packed with awesome sessions, Hands-on Labs, and expert speakers who showcased our latest product releases around the future of VMware Private AI with VMware Cloud Foundation (VCF) 9.1. Attendees got to learn from industry ... Continued The post VMware Explore 2026 in Las Vegas | Day 4 Recap appeared first on VMware Blogs. ## Virgin Atlantic sharpens customer journeys with ChatGPT Work DevFeed: [Virgin Atlantic sharpens customer journeys with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [data](), [dataset]() Tags: [airline](), [analysis](), [analytics](), [chatgpt](), [data](), [explore](), [insights](), [product](), [strategy](), [tools](), [work]() ### AI overview Virgin Atlantic uses ChatGPT Work to connect customer-journey information, accelerate competitive research, support product planning, and turn insights into strategy and digital-experience decisions. ### Source excerpt Virgin Atlantic is accelerating research, product planning, and decision-making with ChatGPT Work, helping teams connect signals across the customer journey. ## Explorers, exploiters, and the myth of the 100x engineer DevFeed: [Explorers, exploiters, and the myth of the 100x engineer]() Original publisher: [Read original article]() Author: Eira May Published: 2026-08-05T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Reinforcement learning](), [leaders-of-code]() Tags: [ai](), [ai-adoption](), [ai-tools](), [business](), [engineering](), [engineering-leadership](), [experiment](), [explore](), [leaders-of-code](), [learning](), [podcast](), [productivity]() ### AI overview The article challenges the myth that a small group of inherently exceptional engineers drives disproportionate AI gains. It argues that engineering leaders should treat exploration and exploitation as a continuum and help more engineers develop through curiosity, adaptability, experimentation, and learning, rather than focusing only on identifying and promoting existing "100x" performers. ### Source excerpt The "find the special ones and promote their traits" approach isn't the best or only way to drive AI adoption and productivity on an engineering team. ## Durable Digest: July highlights DevFeed: [Durable Digest: July highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [releases](), [distributed-systems](), [Cloud Run](), [Langgraph](), [datadog](), [API]() Tags: [announcements](), [api](), [cloud](), [cloud-run](), [datadog](), [distributed-systems](), [explore](), [langgraph](), [releases]() ### AI overview Temporal's July Durable Digest summarizes product releases and updates, including pre-release Serverless Workers for GCP Cloud Run, public-preview LangGraph and LangSmith integrations, generally available billing and metrics APIs, Datadog and Vantage integrations, and improvements to the Worker Status UI. ### Source excerpt Highlights from July include major product releases that make it easier to build and operate durable applications, and enhanced visibility into your Workers. ## Introducing GeneBench-Pro DevFeed: [Introducing GeneBench-Pro]() Original publisher: [Read original article]() Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI research agents](), [AI, ML & Data Engineering]() Tags: [agents](), [ai](), [ai-agents](), [analysis](), [benchmark](), [complexity](), [data](), [datasets](), [diagnostics](), [experimentation](), [explore](), [model](), [models](), [performance](), [research](), [skills](), [support](), [testing](), [workflow]() ### AI overview GeneBench-Pro is a research-level benchmark for evaluating whether AI agents can make judgment-heavy decisions during computational-biology analysis. It uses messy datasets and downstream decision targets to assess ambiguity handling, analytical-path selection, iterative experimentation, and revision of initial plans. ### Source excerpt Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets. ## Our principles DevFeed: [Our principles]() Original publisher: [Read original article]() Published: 2026-04-26T16:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [superintelligence]() Tags: [ai](), [company](), [envision](), [explore](), [superintelligence]() ### AI overview OpenAI presents principles for ensuring that AGI benefits all of humanity, emphasizing democratization, empowerment, user autonomy, and minimizing harm. ### Source excerpt Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work. ## Backstage in Amsterdam: Highlights from BackstageCon and KubeCon + CloudNativeCon Europe 2026 DevFeed: [Backstage in Amsterdam: Highlights from BackstageCon and KubeCon + CloudNativeCon Europe 2026]() Original publisher: [Read original article]() Author: André Wanlin, Emma Indal & Ruxandra Stanciu, Spotify Published: 2026-04-20T00:00:00Z Content type: article Language: en Sources: [Backstage Software Catalog and Developer Platform Blog]() Topics: [Backstage](), [Developer experience](), [internal developer portal](), [Tooling](), [Command-line interface](), [Maintainers](), [pull-requests]() Tags: [2026](), [backstage](), [catalog](), [cli](), [community](), [contributors](), [developer-experience](), [developer-portal](), [ecosystem](), [engineering](), [europe](), [explore](), [idp](), [internal-developer-platform](), [keynote](), [kubecon](), [maintainers](), [open-source](), [platform-engineering](), [plugin](), [plugins](), [pull-requests](), [q-a](), [red-hat](), [software-catalog](), [software-templates](), [techdocs]() ### AI overview A recap of BackstageCon and KubeCon + CloudNativeCon Europe 2026 in Amsterdam highlights community talks, maintainer discussions, a documentary premiere, a live keynote demonstration, and ContribFest. It also covers Backstage deployment governance, unified developer experience, catalog workflows, platform CLI usage, and the plugin ecosystem. ### Source excerpt Backstage in Amsterdam: Highlights from BackstageCon and KubeCon + CloudNativeCon Europe 2026 ## Analyzing data with ChatGPT DevFeed: [Analyzing data with ChatGPT]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Data analysis](), [datasets](), [CSV](), [dashboards]() Tags: [analysis](), [chatgpt](), [dashboards](), [data](), [datasets](), [executive](), [explore](), [generate](), [google](), [how-to](), [insights](), [learn](), [openai-academy](), [product](), [tools](), [work]() ### AI overview Learn how to use ChatGPT to explore datasets, analyze CSV or Excel files, create visualizations, identify anomalies, and turn findings into clear, actionable insights. ### Source excerpt Learn how to explore datasets, generate insights, create visualizations, and turn findings into actionable decisions with ChatGPT. ## Financial services DevFeed: [Financial services]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [Prompt Engineering](), [Data analysis]() Tags: [ai](), [analysis](), [data](), [explore](), [financial-services](), [guides](), [openai](), [openai-academy](), [policy](), [resources](), [scale](), [search](), [tax](), [tools](), [workflows]() ### AI overview A curated collection of OpenAI resources for financial services organizations, covering AI evaluation, adoption, deployment, and scaling in regulated environments. It includes prompts and guidance for financial analysis, research, policy and tax interpretation, document analysis, data extraction, and business workflows. ### Source excerpt Explore AI resources for financial services, including prompt packs, GPTs, guides, and tools to help institutions deploy and scale AI securely. ## New ways to learn math and science in ChatGPT DevFeed: [New ways to learn math and science in ChatGPT]() Original publisher: [Read original article]() Published: 2026-03-10T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [math](), [Artificial Intelligence](), [real-time](), [Graphs]() Tags: [ai](), [chatgpt](), [concepts](), [explore](), [graphs](), [learning](), [math](), [product](), [real-time](), [science]() ### AI overview ChatGPT introduces interactive visual explanations for more than 70 core math and science concepts. Learners can adjust variables and manipulate formulas to see how changes affect graphs and outcomes in real time, making abstract ideas more tangible. ### Source excerpt ChatGPT introduces interactive visual explanations for math and science, helping students explore formulas, variables, and concepts in real time. ## AlphaFold: Five years of impact DevFeed: [AlphaFold: Five years of impact]() Original publisher: [Read original article]() Author: Demis Hassabis; John Jumper; Pushmeet Kohli; Anna Koivuniemi; AlphaFold team Published: 2025-11-25T16:00:12Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Bioinformatics]() Tags: [ai](), [drug-discovery](), [explore](), [predictions](), [research](), [science]() ### AI overview The article examines five years of AlphaFold's impact on biological research, describing faster discovery, more novel protein structures, and increased influence on clinical research and patents. It also presents AlphaFold 3, Isomorphic Labs, and the AlphaFold Server as developments advancing AI-assisted drug discovery and digital biology. ### Source excerpt Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery. ## Firebase MCP Server DevFeed: [Firebase MCP Server]() Original publisher: [Read original article]() Author: Michael Bleigh Published: 2025-05-20T10:47:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [MCP Server](), [Model Context Protocol](), [Firebase CLI](), [Artificial Intelligence]() Tags: [agents](), [ai](), [cloud-messaging](), [cloud-storage](), [experimental](), [explore](), [firebase](), [firebase-cli](), [firestore](), [google-i-o](), [launch](), [mcp](), [mcp-server](), [security-rules]() ### AI overview Firebase announces an experimental Firebase MCP Server, integrated with the Firebase CLI, that lets AI assistants and coding agents use standardized tools to configure, build, manage, and observe Firebase applications. The article describes capabilities including project setup, user management, Firestore access, GraphQL queries, security-rule validation, messaging, Remote Config deployment, crash monitoring, and Cloud Storage URL generation. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Config 2024 in review DevFeed: [Config 2024 in review]() Original publisher: [Read original article]() Author: Dylan Field Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Artificial Intelligence](), [coding]() Tags: [ai](), [component](), [creativity](), [design](), [dev](), [explore](), [features](), [figma](), [generate](), [images](), [prototypes](), [search](), [ui](), [updates]() ### AI overview Figma's Config 2024 recap announces Figma AI, UI3, Figma Slides, and updates to Dev Mode and other Figma features. It highlights AI-assisted visual and asset search, image and copy generation, text rewriting, prototype wiring, layer renaming, and text-prompted UI layout generation. ### Source excerpt We're launching Figma AI, UI3, and Figma Slides, along with major updates to Dev Mode and improvements to your favorite Figma features. ## Rich media input from the keyboard in Compose DevFeed: [Rich media input from the keyboard in Compose]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2024-03-01T12:49:32Z Content type: tutorial Language: en Sources: [Shreyas Patil's Blog]() Topics: [Compose](), [Jetpack Compose](), [GUI](), [App](), [API]() Tags: [android](), [android-app-development](), [android-apps](), [android-development](), [api](), [app](), [applications](), [building](), [chat](), [code](), [components](), [compose](), [content](), [experimental](), [explore](), [feature](), [how-to](), [image](), [images](), [jetpack](), [jetpack-compose](), [keyboard](), [kotlin](), [learn](), [media](), [mobile-apps](), [mobile-development](), [programming-blogs](), [ui](), [uiux]() ### AI overview A tutorial on handling rich media input such as GIFs and images from the keyboard in Jetpack Compose chat applications. It explains that Jetpack Compose 1.7.x introduced an API and modifier for receiving specified content types through keyboard input, with related support for drag-and-drop and clipboard content. ### Source excerpt Learn how to handle rich media input (like GIFs and images) from the keyboard in Jetpack Compose, essential for modern chat applications. ## TensorFlow 2.15 update: hot-fix for Linux installation issue DevFeed: [TensorFlow 2.15 update: hot-fix for Linux installation issue]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-12-05T22:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [pip](), [Linux](), [TensorRT](), [CUDA](), [Python]() Tags: [announcement](), [dependencies](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [nvidia](), [packages](), [release](), [tensorflow](), [tensorflow-core](), [tensorrt](), [update](), [x86-64]() ### AI overview TensorFlow released version 2.15.0.post1 to fix Linux x86_64 installation problems caused by missing TensorRT-related Python dependencies when installing TensorFlow 2.15 with NVIDIA CUDA dependencies via pip. The hotfix restores the intended installation behavior, while users pinning versions must specify 2.15.0.post1 explicitly. ### Source excerpt Posted by the TensorFlow team We are releasing a hot-fix for an installation issue affecting the TensorFlow installation process. The TensorFlow 2.15.0 Python package was released such that it requested tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags. This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda]. Depending on the installation method, TensorFlow 2.14 would be installed instead of 2.15, or users could receive an installation error due to those missing dependencies. To solve this issue as quickly as possible, we have released TensorFlow 2.15.0.post1 for the Linux x86_64 platform. This version removes the tensorrt Python package dependencies from the tensorflow[and-cuda] installation method. Support for TensorRT is otherwise unaffected as long as TensorRT is already installed on the system. Now, pip install tensorflow[and-cuda] works as originally intended for TensorFlow 2.15. Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please be aware of the following caveat: for users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, tensorflow[and-cuda]==2.15.0 will not install this fixed version. Please use ==2.15.0.post1 to specify this exact version on Linux platforms, or a fuzzy version specification, such as ==2.15.*, to specify the most recent compatible version of TensorFlow 2.15 on all platforms. ## What's new in TensorFlow 2.15 DevFeed: [What's new in TensorFlow 2.15]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-17T18:55:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [CUDA](), [cpu](), [Linux](), [pip](), [Windows](), [Compiler]() Tags: [announcement](), [compiler](), [cpu](), [cuda](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [performance](), [release](), [tensorflow](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.15 introduces simpler optional installation of NVIDIA CUDA libraries through pip on Linux, provided the NVIDIA driver is already installed. It also enables oneDNN CPU optimizations by default for Windows x64 and x86 packages, adds full tf.function type support, upgrades CUDA to 12.2, and uses Clang 17 for package builds and source compilation. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.15 has been released! Highlights of this release (and 2.14) include a much simpler installation method for NVIDIA CUDA libraries for Linux, oneDNN CPU performance optimizations for Windows x64 and x86, full availability of tf.function types, an upgrade to Clang 17.0.1, and much more! For the full release note, please check here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please check here. TensorFlow Core NVIDIA CUDA libraries for Linux The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip. As long as the NVIDIA driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's NVIDIA CUDA library dependencies in the Python environment. Aside from the NVIDIA driver, no other pre-existing NVIDIA CUDA packages are necessary. In TensorFlow 2.15, CUDA has been upgraded to version 12.2. oneDNN CPU performance optimizations For Windows x64 & x86 packages, oneDNN optimizations are now enabled by default on X86 CPUs. These optimizations can be enabled or disabled by setting the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, simply unset the environment variable. tf.function tf.function types are now fully available. tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support. Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.function's and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details. Introducing tf.types.experimental.AtomicFunction as the fastest way to perform TF computations in Python. ## Join us at the third Women in ML Symposium! DevFeed: [Join us at the third Women in ML Symposium!]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-17T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Google AI]() Tags: [ai](), [announcement](), [artificial-intelligence](), [community](), [event](), [explore](), [generative-ai](), [google-ai](), [machine-learning](), [women-in-ml]() ### AI overview An invitation to the third Women in Machine Learning Symposium, a virtual event covering machine learning, AI, generative AI, privacy-preserving AI, accelerators, ML frameworks, and practical applications. ### Source excerpt Posted by Sharbani Roy - Senior Director, Product Management, Google We're back with the third annual Women in Machine Learning Symposium on December 7, 2023! Join us virtually from 9:30 am to 1:00 pm PT for an immersive and insightful set of deep dives for every level of Machine Learning experience. The Women in ML Symposium is an inclusive event for anyone passionate about the transformative fields of Machine Learning (ML) and Artificial Intelligence (AI). Dive into the latest advancements in generative AI, explore the intricacies of privacy-preserving AI, dig into the underlying accelerators and ML frameworks that power models, and uncover practical applications of ML across multiple industries. Our event offers sessions for all expertise levels, from beginners to advanced practitioners. Hear about what's new in ML and building with Google AI from our keynote speakers, gain insights from seasoned industry leaders across Google Health, Nvidia, Adobe, and more - and discover a wealth of knowledge on topics ranging from foundational AI concepts to open source tools, techniques, and beyond. RSVP today to secure your spot and explore our exciting agenda. We can't wait to see you there! ## Unlocking the power of an inclusive classroom with Figma's open canvas DevFeed: [Unlocking the power of an inclusive classroom with Figma's open canvas]() Original publisher: [Read original article]() Author: Rebecca Ackermann Published: 2023-09-28T00:00:00Z Content type: opinion Language: en Sources: [Figma Blog]() Topics: [Figma](), [Canvas]() Tags: [education](), [explore](), [figma](), [integration](), [learning](), [open](), [remote](), [tools]() ### AI overview Two educators discuss how Figma's open canvas and other visual collaborative tools can support more inclusive, accessible learning. The article follows Lisa Highfill's shift from rigid, standardized lesson plans toward purposeful, choice-driven learning, including digital Hyperdocs and tools for remote and hybrid classrooms. ### Source excerpt Two educators explain why visual collaborative tools, like Figma, are making learning more accessible to everyone, and why teaching by the book is a thing of the past. ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### AI overview The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms. ### Source excerpt Posted by Paul Ruiz, Developer Relations Engineer We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future _loadModel() async { final opt ## Explore-exploit dilemma in Ranking model DevFeed: [Explore-exploit dilemma in Ranking model]() Original publisher: [Read original article]() Author: Aida Orujova Data Scientist Published: 2022-11-04T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [recommendation systems](), [Algorithms](), [Data Science]() Tags: [algorithm](), [conversion](), [data](), [data-science](), [explore](), [machine-learning](), [ranking]() ### AI overview The article explains the explore-exploit dilemma in trivago's accommodation ranking model. It describes balancing historically strong-performing accommodations with previously unseen inventory to improve relevance and discover higher-potential options, using the multi-armed bandit problem as a framing. ### Source excerpt Imagine, out of thousands of accommodations that match a user search, you have to select the "best" 25 to show to the user. Which ones would you show- the ones you know perform well or ones that... ## Interactive components: Less wiring, more inspiring DevFeed: [Interactive components: Less wiring, more inspiring]() Original publisher: [Read original article]() Author: Nikolas Klein Published: 2021-10-26T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Process]() Tags: [community](), [components](), [design](), [examples](), [explore](), [flow](), [libraries](), [performance](), [prototypes](), [testing]() ### AI overview Figma introduces interactive components as a generally available feature, allowing designers to define interactions and animations between component variants. The update aims to make prototyping faster and more faithful by reducing manual wiring and frames, enabling direct editing of component states, supporting reusable library elements, and improving performance, stability, observation mode, and auto layout. ### Source excerpt Today, we're introducing interactive components and a handful of smaller improvements to empower teams to more quickly explore, iterate, and share. ## How (and why) we built branching DevFeed: [How (and why) we built branching]() Original publisher: [Read original article]() Author: John Fuetsch Published: 2021-10-12T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Cloud](), [software-development]() Tags: [cloud](), [collaboration](), [design](), [experimental](), [explore](), [scale]() ### AI overview Figma explains how it built branching to support collaboration at scale. Branches provide exploratory spaces for designers to iterate without changing the main file until work is ready and approved, while preserving approved designs and supporting experimentation, design libraries, and stakeholder previews. ### Source excerpt In April, we introduced branching as a way to enable collaboration at scale. Today, branching is broadly available on our Organization plan. [Next page]()