# image Published articles for image. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Nvidia представила Axolotl3D -- модель для достраивания скрытых частей 3D-объектов DevFeed: [Nvidia представила Axolotl3D -- модель для достраивания скрытых частей 3D-объектов]() Original publisher: [Read original article]() Author: daniilshat Published: 2026-09-17T20:54:47Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [3D](), [Nvidia]() Tags: [3d](), [3d-66bde4d200c5](), [axolotl3d](), [image](), [nvidia](), [tag-055aee430837](), [tag-13e2af703774](), [tag-1cd610c0e518](), [tag-86b843454893](), [tag-8e9a901cca08]() ### AI overview Nvidia introduced Axolotl3D, a generative model that reconstructs and edits 3D objects from incomplete data. It combines multiple images, camera positions, visibility masks, and partial point clouds to restore missing geometry. The model was trained on 407,000 models, but its code and weights have not yet been published. ### Source excerpt Nvidia представила Axolotl3D -- генеративную модель для восстановления и редактирования 3D-объектов по неполным данным. Она учитывает несколько фотографий, положение камеры, маски видимости и частичное облако точек, а после восстанавливает недостающую геометрию. Читать далее ## Hackers Stole Flock's Camera Software, Revealing How the Company Tracks Cars and People DevFeed: [Hackers Stole Flock's Camera Software, Revealing How the Company Tracks Cars and People]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-17T16:04:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [Computer vision](), [Encryption](), [Software](), [data](), [Cryptography]() Tags: [camera](), [encryption](), [flock](), [image](), [logs](), [net-11](), [people](), [privacy](), [software](), [vision]() ### AI overview A joint investigation analyzed data copied from a Flock roadway camera after hackers breached the device. The recovered files showed that its on-device software detects people, vehicles, license plates, bicycles, and some graphics, while logs documented extensive image generation and vehicle activity. ### Source excerpt "Hackers ripped down a Flock camera above a roadway, made a near-complete copy of the data stored inside it, and shared the files with 404 Media and WIRED," according to an article published on both sites. Though Flock has described its system as protected by on-device encryption, "The hackers were able to copy the camera's storage and recover an encryption key stored on the device, which unlocked videos of thousands of vehicle detections." The hackers shared the material with 404 Media and the transparency nonprofit Distributed Denial of Secrets, which shared the data with WIRED. 404 Media and WIRED then analyzed those files as part of a joint investigation... [T]he joint analysis of the recovered data shows that software running on the device explicitly detects people as well as vehicles, license plates, and bicycles. The camera can produce dozens of images of a single passing vehicle and, according to several weeks of recovered logs, generated more than a million images. Its computer-vision software also sometimes isolated bumper stickers and other graphics, including, in one case, an American flag patch on a motorcyclist's saddlebag... According to our analysis, the camera's logs recorded about 21 days of activity across several periods. During those windows, the device photographed roughly 50,200 vehicles and generated about 1.6 million images. On a typical day, it logged around 3,300 vehicles, with a high of 4,454... The software running on the camera explicitly detects people, something which is typically overlooked in discussions around Flock cameras. When it spots a person, it records where they appear in the image and how confident it is in the detection. It was a collective calling itself stegan0gram that breached the cameras, according to the interview they did with Wired and 404 Media. "Why just destroy them when we can reverse engineer them and find the secrets of those spying on us?" Read more of this story at Slashdot. ## Mir/Wayland-Powered Miracle-WM 0.11 Released With New Overview Mode, Window Urgency DevFeed: [Mir/Wayland-Powered Miracle-WM 0.11 Released With New Overview Mode, Window Urgency]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T00:58:42Z Content type: release Language: en Sources: [Phoronix]() Topics: [Wayland](), [Library](), [canonical]() Tags: [commands](), [data](), [desktop-linux](), [events](), [github](), [image](), [input](), [ipc](), [library](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [overview](), [phoronix](), [phoronix-test-suite](), [plugins](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [update](), [wayland](), [window](), [windows]() ### AI overview Miracle-WM 0.11, a Wayland compositor built on the Mir library, adds an overview mode, window urgency support, plugin IPC events and commands, and support for newer Mir protocols. The release is built on Mir 2.29. ### Source excerpt Canonical engineer Matthew Kosarek rolled out Miracle-WM 0.11 today as an end-of-summer update to this Wayland compositor built atop the Mir library... ## Making thumbnails fast DevFeed: [Making thumbnails fast]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-09-14T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Image](), [pixel](), [Server](), [Software]() Tags: [article](), [blog](), [code](), [color](), [components](), [design](), [engineering](), [image](), [images](), [news](), [rgb](), [software](), [web-frontend]() ### AI overview Khan Academy explains how it generated custom video thumbnails by resizing, desaturating, lightening, color-tinting, and overlaying text and branding on source images. The post also explains pixel multiplication and why the team implemented the image operations themselves because its server infrastructure could not use available libraries. ### Source excerpt Note: This post contains quite a bit of LaTeX notation, which is not supported right now on this ... Read more ## KDE Plasma 6.8 Remote Desktop To Enjoy Lower Latency Performance DevFeed: [KDE Plasma 6.8 Remote Desktop To Enjoy Lower Latency Performance]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-12T10:07:48Z Content type: news Language: en Sources: [Phoronix]() Topics: [Latency](), [client](), [Wayland](), [Protocol (disambiguation)](), [configuration](), [GUI]() Tags: [configuration](), [desktop](), [desktop-linux](), [image](), [latency](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [remote](), [switching](), [time](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview KDE Plasma 6.8 improves KRDP remote desktop latency by dropping incoming frames when the encoder outpaces the client. The release also includes input-method, clipboard, Wayland protocol, wallpaper dialog, and XWayland session configuration fixes. ### Source excerpt Along with KDE Plasma 6.8 beta releasing this week, there were some other interesting Plasma changes this week worthy of a shout-out... ## Automatic image cropping in Appwrite with AutoGravity DevFeed: [Automatic image cropping in Appwrite with AutoGravity]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Computer vision](), [Code](), [Go]() Tags: [announcements](), [automatic](), [computer-vision](), [go](), [image](), [object-detection](), [open-source](), [storage](), [web]() ### AI overview Appwrite introduces AutoGravity, an open-source Go service that adds automatic image cropping to Appwrite Storage. It uses a model pipeline to identify a focal point and returns normalized coordinates that Appwrite's existing image transformation pipeline uses for cropping. The article explains that transformed images are cached and identifies U²-Net as the main model behind the feature. ### Source excerpt AutoGravity brings automatic image cropping to Appwrite Storage. Learn how saliency detection and face detection pick the focal point behind gravity=auto. ## GPT Image 2.5 Flare and Sunburst now available on AI Gateway DevFeed: [GPT Image 2.5 Flare and Sunburst now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-08T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [OpenAI]() Tags: [ai](), [generation](), [gpt](), [image](), [images](), [models](), [openai](), [playground]() ### AI overview OpenAI's GPT Image 2.5 Flare and GPT Image 2.5 Sunburst are now available through AI Gateway for image generation and editing. Flare emphasizes faster generation, while Sunburst prioritizes editing precision; both support text prompts, reference images, detailed instructions, complex layouts, transparent backgrounds, and targeted edits that preserve the rest of an image. ### Source excerpt GPT Image 2.5 Flare and GPT Image 2.5 Sunburst from OpenAI are now available on AI Gateway. Both models accept text prompts and reference images for generation and editing. They produce natural lighting and textures, follow detailed visual instructions, handle complex layouts and transparent backgrounds, and make targeted edits while preserving the rest of an image. Choose Flare for faster generation and Sunburst when editing precision matters most. For Flare, use the model ID openai/gpt-image-2.5-flare: For Sunburst, use openai/gpt-image-2.5-sunburst. Pass a reference image with the instruction: Try Flare or Sunburst in the model playground, or view all image models available on AI Gateway. Read more ## Compute that takes any shape DevFeed: [Compute that takes any shape]() Original publisher: [Read original article]() Author: Luke Phillips-Sheard Published: 2026-09-01T07:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Cloud](), [Provisioning](), [cloud-infrastructure](), [Vercel](), [cpu](), [IO](), [DRIVE]() Tags: [agent](), [cloud](), [code](), [compute](), [cpu](), [image](), [infrastructure](), [io](), [memory](), [pricing](), [real-time](), [sandbox](), [storage](), [vercel]() ### AI overview Vercel presents Fluid as a unified compute system that assembles machines for different workloads, changes configuration dynamically, and absorbs burst capacity in real time. It supports builds, sandboxes, and functions, using isolated virtual machines, custom images, and separate storage connections. ### Source excerpt Workloads differ in how long they run, how much memory they take, and how much of the environment they control. That previously meant a different compute primitive for each job, so product iterations required provisioning infrastructure, not just working on a feature. We built Vercel so that none of this has to be your problem. The compute layer is a single system with a simple job. Take any workload, assemble the machine it needs, swap configuration on the fly, and absorb burst capacity in real time. We call this Fluid. Builds ran on it first, then sandboxes, and now functions. If you've shipped on Vercel, you've been on Fluid without knowing it. Fluid compute now runs over 15 million builds a day, 25 million sandboxes a week, and a trillion requests a month. A history of compute Changing a machine used to mean changing it by hand. You went down to the computer store, bought a hard drive or a stick of RAM, and swapped it in yourself. Then you could rent bare metal when you needed it, and use it to serve a website, host a database, or send email. Then the cloud let you request a machine in any configuration you wanted, choose its operating system, use it, and throw it away when you were done. For agents, even the cloud is too slow. A standard VM can't provision fast enough to keep up with how they work, but this is where Fluid excels. How a workload runs on Fluid When a workload comes in, Fluid assembles a machine to fit its shape. If an agent needs to run code, Hive provides an isolated VM, usually one already warm, so it's ready instantly. Your own image boots on top as the environment. A Drive connects, and your files are right where you left them, because storage was never tied to the machine. Each workload needs something different. A build is compute-bound, so it wants a beefy machine, heavy on CPU and memory. A function is IO-bound, loading specific code to run the instant a request lands, usually on a small VM. A sandbox is flexibility-bound, taking whatever ## MiniMax H3 and H3 Max are 50% off on AI Gateway DevFeed: [MiniMax H3 and H3 Max are 50% off on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [API](), [API keys](), [dashboards](), [browser]() Tags: [ai](), [ai-gateway](), [api](), [audio](), [browser](), [generate](), [generation](), [image](), [model](), [models](), [pricing]() ### AI overview MiniMax H3 and H3 Max receive a 50% discount on Vercel AI Gateway from August 30 through September 13. H3 supports 2K video generation from text, images, video, and audio inputs, while H3 Max offers faster 480p and 768p rendering from text or a starting image. Existing model IDs remain unchanged, so no code changes are required. ### Source excerpt MiniMax H3 and H3 Max are 50% off on AI Gateway from August 30 through September 13, in partnership with MiniMax. The discount covers requests billed through AI Gateway, at every duration and in every aspect ratio the model supports. H3 generates 2K video from a text prompt, a starting image, a pair of first and last frames, or reference images, video, and audio. H3 Max trades resolution for speed: it renders faster at 480p and 768p, and it takes a text prompt or a starting image. The model IDs (minimax/minimax-h3 and minimax/minimax-h3-max) are unchanged, so requests you already send pick up the discounted rate with no code change: Renders take minutes, so poll runs the generation as a background job and makes short status requests until it lands, rather than holding one long request open. See asynchronous generation for the webhook and start-and-status routes. Get started Create an API key in the AI Gateway section of your dashboard, or generate a clip in the browser first from the model playground. Current rates for every model are on the pricing page. You can view all video models available on AI Gateway. Read more ## Muse Image now available on AI Gateway DevFeed: [Muse Image now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [Meta](), [vercel ai sdk](), [API](), [superintelligence](), [Inference]() Tags: [ai](), [ai-gateway](), [api](), [image](), [inference](), [meta](), [meta-muse](), [model](), [models](), [muse](), [pricing](), [sdk](), [superintelligence](), [uptime]() ### AI overview Muse Image, Meta Superintelligence Labs' first image model, is now available through Vercel's AI Gateway. It supports both image generation from prompts and instruction-based image editing, including reference images, through the AI SDK. AI Gateway also provides unified model access, usage and cost tracking, failover, routing, reporting, budgets, and performance optimizations without markup or platform fees on inference. ### Source excerpt Muse Image from Meta Superintelligence Labs is now available on AI Gateway. It is their first image model and a separate family from Muse Spark, returning images rather than text. Send a prompt and get an image back, or send an image with an instruction and get it changed. One model does both, so you don't switch models to move from generating to editing. To use Muse Image, set model to meta/muse-image-1.0 and call generateImage from the AI SDK: To steer the result toward art you already have, pass reference images in prompt.images alongside the text, and the model blends them into what it draws. Editing Pass the image you want changed in prompt.images with an instruction, and the model changes what you asked for and leaves the rest: Try Muse Image in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, 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. You can view all image models available on AI Gateway. Read more ## How Yandex combined image and document text signals for image-search ranking DevFeed: [How Yandex combined image and document text signals for image-search ranking]() Original publisher: [Read original article]() Author: nikolaevkona (Яндекс) Published: 2026-08-10T08:00:19Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [яндекс](), [vlm](), [Image](), [realtime]() Tags: [image](), [machine-learning](), [ml](), [realtime](), [tag-4004cf5948d3](), [tag-8be2dbf54d97](), [vlm](), [yandex-e983188bc433]() ### AI overview Yandex describes using multimodal vision-language models to jointly assess an image and its associated document text for image-search ranking. The team distilled a larger model into lighter models for different pipeline stages and reports deployment in real-time search. ### Source excerpt Исторически в Яндекс Картинках релевантность документа оценивалась по двум сигналам: насколько запросу подходит само изображение и насколько -- текст, связанный с этим изображением. Такой подход позволяет учесть контент картинки и не провалиться на визуально трудноотличимых объектах, однако он же порождает проблему: в "серой зоне", когда текстовая релевантность не сонаправлена с картиночной, становится неочевидно, как именно агрегировать сигналы в финальный скор релевантности. Привет! Я Константин Николаев, занимаюсь внедрением нейротехнологий в Поиске по картинкам. В этой статье я расскажу, как наша команда научила модели смотреть на картинку и читать текст документа одновременно: начали с тяжёлой мультимодальной VLM ради максимального качества, а затем дистиллировали её в набор лёгких моделей -- по одной под каждую стадию пайплайна. Что из этого удалось довести до realtime-поиска с десятками тысяч запросов в секунду и как совместный анализ двух модальностей добавил 5% релевантных картинок в топ выдачи -- под катом. Читать далее ## Seedance 2.5 now available on Vercel AI Gateway DevFeed: [Seedance 2.5 now available on Vercel AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Artificial Intelligence](), [vercel ai sdk](), [SDKs](), [API]() Tags: [ai](), [ai-gateway](), [api](), [audio](), [generate](), [image](), [model](), [sdk](), [vercel](), [video]() ### AI overview Vercel AI Gateway now offers ByteDance's Seedance 2.5 video model. The model generates clips up to 30 seconds with synchronized audio, supports up to 50 multimodal reference inputs, can extend short clips, and can edit videos while preserving framing, camera movement, and pacing. The article also documents video generation, image and video references, and video editing through the AI SDK. ### Source excerpt Seedance 2.5 from ByteDance is now available on AI Gateway. It can generate up to 30-second clips with synchronized audio, and takes up to 50 reference inputs in one request, spanning images, video, audio, and style. A single clip holds camera movement and continuity without stitching shots together in post, and short clips can be extended, carrying over character, scene, and camera movement. Prompts work in more than ten languages. Seedance 2.5 can also edit a finished video in place, swapping backgrounds, products, or characters while the frame, camera, and pacing stay fixed, so one shoot yields several variants. Generating a video Set model to bytedance/seedance-2.5 and call generateVideo from the AI SDK: Adding image references Pass image URLs in inputReferences and point at them in the prompt with [Image 1], [Image 2]. Tag each entry with its media type. Seedance treats an untagged URL as an image. Combining reference types Mix videos and images in the same request to pull different qualities from each: motion and camera work from a clip, a subject's appearance from a still. Videos go in the same inputReferences array as images, tagged with a video media type. Numbering runs per type so the first video is [Video 1] and the first image is [Image 1], and both can appear in one prompt. Editing a video Referencing and editing use the same setup, so the prompt decides which you get. Ask the model to follow a clip's camera movement and it builds a new video from scratch. Ask it to change something in the clip and it edits the video you gave it. You can reference and edit in one prompt, though results get less predictable, so it helps to be explicit about what each asset is for. For the playground and API reference, see the model page, or browse every video model on AI Gateway. Read more ## Name that Ware, July 2026 DevFeed: [Name that Ware, July 2026]() Original publisher: [Read original article]() Author: bunnie Published: 2026-07-30T14:52:35Z Content type: opinion Language: en Sources: [bunnie's blog]() Topics: [SOC](), [structure]() Tags: [2026](), [image](), [name-that-ware](), [soc](), [structure]() ### AI overview A July 2026 "Name that Ware" puzzle asks readers to identify a conserved design pattern in the lower-left area of a die-shot excerpt from an SoC and explain why the structures are organized that way. The document does not provide the answer. ### Source excerpt The Ware for July 2026 is shown below. I've got silicon on the brain, so I'm going to give a die shot another go at name that ware. Hopefully this one is a bit easier than the last one. This excerpt is a design pattern I look for and find on almost every SoC. It's [...] ## Pure Codename One Text Editing Without Native Overlays DevFeed: [Pure Codename One Text Editing Without Native Overlays]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [ui](), [Code]() Tags: [component](), [formatting](), [gif](), [image](), [jpeg](), [native](), [port]() ### AI overview A new semantic port contract lets Codename One handle text painting, selection, rich editing, clipboard formats, and bidirectional layout while platform ports provide keyboard and input-method events. The opt-in path supports text composition, undo and redo, selection geometry, and multi-format clipboard content without a visible native field. ### Source excerpt A semantic port contract lets Codename One own text painting, selection, rich editing, clipboard formats, and bidirectional layout while the platform supplies keyboard and IME operations. ## Seedream 5.0 Pro is now available on AI Gateway DevFeed: [Seedream 5.0 Pro is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-11T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [API](), [model-deployment](), [SDKs]() Tags: [ai](), [ai-gateway](), [api](), [api-keys](), [cost](), [generation](), [image](), [images](), [inference](), [models](), [performance](), [routing](), [tokens](), [uptime]() ### AI overview Seedream 5.0 Pro is now available through Vercel AI Gateway as an image generation and editing model. It can create text-to-image outputs with accurate text rendering, typographic formatting, dense infographics, and realistic imagery. The article also describes AI Gateway features for model access, usage and cost tracking, retries, failover, reporting, API key budgets, routing, and model discovery through a playground and leaderboard. ### Source excerpt Seedream 5.0 Pro is now available on AI Gateway. Seedream 5.0 Pro is an image generation and editing model. It generates images from text, rendering text without spelling errors and following typographic rules, and produces dense infographics with charts, timelines, and layouts alongside realistic imagery. To use Seedream 5.0 Pro, set model to bytedance/seedream-5.0-pro 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 Seedream 5.0 Pro in the model playground. Read more ## 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 ## DeveloperHub Introduces a New Editor for More Reliable Documentation Authoring DevFeed: [DeveloperHub Introduces a New Editor for More Reliable Documentation Authoring]() Original publisher: [Read original article]() Author: Zaid Daba'een Published: 2026-06-30T09:47:09Z Content type: release Language: en Sources: [DeveloperHub.io]() Topics: [Documentation](), [Tool](), [formatting](), [render]() Tags: [commands](), [docs](), [documentation](), [editor](), [engineering](), [features](), [google](), [image](), [inside](), [model]() ### AI overview DeveloperHub has rebuilt its editor around a single document model. The new editor is designed to preserve saved formatting, improve undo and redo, handle pasted content more faithfully, and provide tools such as slash commands, link controls, an image library, variable and glossary pickers, blocks within lists, and keyboard-focused editing. ### Source excerpt We rebuilt the DeveloperHub editor from the ground up. Here is what is new, and why it makes writing and maintaining your documentation noticeably better. ## 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 ## Podman 6 Configuration File Changes DevFeed: [Podman 6 Configuration File Changes]() Original publisher: [Read original article]() Author: Paul Holzinger Published: 2026-06-22T16:14:55Z Content type: article Language: en Sources: [blog.podman.io]() Topics: [podman](), [configuration](), [Containers]() Tags: [announcement](), [blog](), [buildah](), [code](), [configuration](), [containers](), [files](), [git](), [go](), [image](), [libraries](), [podman](), [registry](), [releases](), [root](), [skopeo](), [storage]() ### AI overview This article introduces the major configuration-file rework planned for Podman 6. The changes affect Podman, Buildah, Skopeo, and shared container libraries, including how files such as containers.conf, registries.conf, storage.conf, policy.json, registries.d, and certs.d are handled and parsed. ### Source excerpt Podman 6 is about to be released soon, so I would like to talk about the biggest change we made: a major rework of how we handle and parse our configuration files. This does not just affect Podman but also Buildah, Skopeo, and many other tools building on top of our underlying go.podman.io/image and go.podman.io/storage [...] ## Extract Text from Your PDF and Image Files with Apache Tika DevFeed: [Extract Text from Your PDF and Image Files with Apache Tika]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-06-13T00:45:07Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Docker Compose](), [Docker](), [container](), [AI search]() Tags: [ai](), [browser](), [command-line](), [container](), [docker](), [how-to](), [image](), [pdf](), [rag](), [services](), [xml]() ### AI overview This tutorial explains how to deploy Apache Tika with Docker or Docker Compose and use it to extract text from PDF, image, and other document formats. The extracted text can provide plaintext context for local AI tools and RAG workflows. ### Source excerpt With AI becoming increasingly popular in everything, and retrieval-augmented generation (RAG) becoming a requirement in everyone's organization, how you're providing context to the AI tools becomes im... The post Extract Text from Your PDF and Image Files with Apache Tika appeared first on The Polyglot Developer. ## Google Drive on Linux: A Fast rclone Mount (and Why Your Thumbnails Are Missing) DevFeed: [Google Drive on Linux: A Fast rclone Mount (and Why Your Thumbnails Are Missing)]() Original publisher: [Read original article]() Author: Ivan Morgillo Published: 2026-06-12T10:00:00Z Content type: tutorial Language: en Sources: [Ivan Morgillo]() Topics: [Google](), [Linux](), [mount](), [Ubuntu](), [systemd](), [Caching](), [xfce](), [OAuth](), [API]() Tags: [browser](), [caching](), [google](), [image](), [linux](), [mount](), [oauth](), [systemd](), [ubuntu](), [xfce]() ### AI overview A tutorial describing a personal setup for mounting Google Drive as a browsable folder on Ubuntu with XFCE using rclone. It covers OAuth authorization, a systemd user service, aggressive caching for faster browsing, and the problem of missing image thumbnails on the mount. ### Source excerpt How I finally got Google Drive working as a fast, normal folder on Linux with rclone -- and fixed the non-obvious problem of missing image thumbnails on FUSE mounts. ## Journey to JPEG XL: How open source experiments shaped the future of image coding DevFeed: [Journey to JPEG XL: How open source experiments shaped the future of image coding]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-03T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [coding](), [Compression](), [Open Source](), [Optimization](), [color](), [Internet]() Tags: [coding](), [color](), [compression](), [image](), [image-compression](), [internet](), [jpeg](), [jpeg-xl](), [open-source](), [optimization](), [web]() ### AI overview The article traces the development of JPEG XL through earlier open-source experiments, including WebP Lossless, Brotli, Butteraugli, Guetzli, and Brunsli. It explains how work on entropy coding, context modeling, psychovisual metrics, color representation, and JPEG compression informed the newer image format. ### Source excerpt by Jyrki Alakuijala, Zoltán Szabadka & Luca Versari, Paradigms of Intelligence, Google Technology & Society Building the Next Generation Image Standard The internet runs on images. Since the early days of the web, there has been a relentless tension between visual fidelity and bandwidth. For decades, the industry relied on the venerable JPEG standard for images loading fast. It served us remarkably well, but as displays moved to High Dynamic Range (HDR) and Wide Color Gamut (WCG), the format began to show its limits. The road to JPEG XL (JXL) wasn't a straight line. It was a decade-long exploration, creating a series of milestone projects testing radical ideas in psychovisual modeling, entropy coding, and optimization. Today, as JPEG XL sees rapid adoption across operating systems and professional standards, we're looking back at the experiments that made it possible. The Early Foundation: 2011-2017 Our study began with a focus on understanding the limits of existing technology. We didn't start by trying to write a new standard; we started by trying to make the current ones better, and learning their limitations. This allowed us to make the new formalism more flexible and efficient in the right places. WebP Lossless and Brotli: Lossy WebP drew its lineage from video technology, the WebP Lossless (2011) represented an architectural and scoping departure. We debuted the entropy image concept, an innovative method utilizing a secondary image to orchestrate the selection of static entropy codes for the primary visual data. We reapplied this approach later with data-driven context modeling in the Brotli compression format, enabling rich context modeling without slowing decoding. Butteraugli: Around 2014, we realized that raw mathematical compression (PSNR) wasn't enough, and simple psychovisual approximations (SSIM and similar) failed in color-rich environments. We built Butteraugli and the XYB color space to mimic the human visual system's edge detection and opponent-co ## How we improved image download sizes on Medium with just four characters DevFeed: [How we improved image download sizes on Medium with just four characters]() Original publisher: [Read original article]() Author: Scott Batson Published: 2026-05-18T15:29:29Z Content type: article Language: en Sources: [Medium]() Topics: [Web Development](), [HTML](), [Image](), [JavaScript](), [Server-side rendering](), [Angular](), [Ember](), [Single-page application (SPA)]() Tags: [angular](), [html](), [image](), [javascript](), [programming](), [render](), [server-side-rendering](), [technology](), [time](), [web-development]() ### AI overview The article explains how modern HTML image features, including and srcset, can select images based on browser capabilities, screen dimensions, file types, layout, and color-scheme preferences. It contrasts these features with older JavaScript-based approaches to serving optimized images. ### Source excerpt HTML images just got better, with the subtlest change to your codebasePhoto by Adi Suyatno I know what you're thinking. Scott, I landed on Medium on a small but not mobile-small display and the download chunk related to images in my home feed was like 7% smaller. First of all, thank you for noticing. Second, what if I told you that you too can improve your total asset download size by adding just four characters to your code base? Sounds too good to be true, right? Well, I'm here to tell you, it's true... after I explain how we got here. The history of images on the web As a web developer, you've probably spent a lot of time thinking about and fighting with images. According to the HTTP Archive, even in today's modern landscape of "JavaScript everywhere" the majority of most website's transfer size is images. We've moved away from the "hero image on every page" ethos, but still we're an image-heavy internet. When I was getting my start in web development in 2010ish, we were still adapting to a "mobile-friendly" world. The majority of the web was built on WordPress. We didn't have a way back then to dynamically load an optimized image. 3G connections were the primary speed in the US for viewing the internet, so the practice was images "need to work on mobile" regardless of the user's device. For those of us writing Angular and Ember (because React wasn't a thing yet), you had to legitimately render the correct src based on context you could get from JavaScript. {{#if browserSmal}} {{else}} {{/if}} This is back when we wrote handlebars for our markup and the web was filling up with SPAs with no server-side-rendering. We did it this way because HTML lacked a way for specifying how we wanted to optimize images. Then in 2012, RICG introduced 2 new specs for image optimization, with 2 different use cases: elements and srcset. Picture Elements I love the element. It gives us a lot of control ## Seasons time-lapse - the foundations DevFeed: [Seasons time-lapse - the foundations]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-17T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Python](), [OpenCV](), [Vibe coding](), [Inference]() Tags: [art](), [development](), [image](), [llm](), [photos](), [python](), [technical](), [time](), [time-lapse](), [video]() ### AI overview This article introduces a multi-part retrospective on building a seasons time-lapse video from photographs taken over several years. It describes using an LLM-assisted workflow, choosing Python, and designing a typed, tested image-processing pipeline that inventories, filters, aligns, and orders photos. ### Source excerpt I live close to nature. I regularly go for a run in the countryside. Over several years, during my runs, I've taken pictures from the same position, always roughly the same angle. I had a vague idea in the back of my mind, as an 'artistic' project. One day, I'd turn those photos into a time-lapse video, one that would show the passage of seasons across a single place. Spoiler, here's the work in progress: However, I knew that this project would take ages. [Next page]()