Design AI Tools

Capnot is a macOS app that adds a caption-first communication layer on top of whatever is on your screen. It places live captions, caption bubbles, drawings, and pointer tools over any app running on your Mac, so a screen demo can still make sense when the sound is off. The product is built for people who explain through screens — SaaS founders and indie developers shipping product demos, presenters, remote workers, educators, and streamers — and its stated purpose is to let you record once and explain it live, with no voice-over and no post-production. Every tool is one hotkey away from a menu bar app, and the captions and annotations appear on screen instantly in whatever recording or screen share you are already running. The website frames the core problem simply: your screen is visible, but your meaning is not. It describes three specific failures. First, the demo is done but the editing is not — you finish a walkthrough and then lose another hour adding captions, arrows, and zooms in a video editor. Second, no mic means no explanation, so a noisy room, a muted call, or a silent social post stops you from explaining what is on screen. Third, viewers lose the tiny cursor and miss the button, menu, or detail you meant to show, which breaks the explanation. Capnot's answer is to add the missing communication layer while you record, share, or present, rather than afterwards in an editor. The Caption toolkit covers everything you want to say, on screen, right now. Bubble Chat (hotkey ⌃/) creates a caption bubble anchored to your cursor; you click anywhere to plant it on screen so viewers read your message next to the thing you are talking about, with no "look at the top-left corner" confusion. Dialogue Mode (⌃.) presents like a broadcaster, showing your name, photo, and title above a polished subtitle panel at the bottom of the screen while you speak naturally; you can choose from built-in style presets or design your own, and toggle Dialogue Mode off for a clean subtitle-only look. With the broadcaster overlay off, you get the Subtitle Bar: a slim, unobtrusive, readable bar at the bottom of the screen, style-matched to your brand. Live Captions, marked Beta, let you hit the mic and speak naturally while Capnot transcribes you in real time via macOS Speech Recognition, fully on-device, so no audio leaves your Mac and there is no cloud or subscription involved. The Annotation toolkit lets you draw attention to what actually matters by overlaying your screen with drawings and shapes, with no app switch and one hotkey. The tool list includes a Move tool to reposition shapes, a Pen for freehand drawing, an Arrow to point to anything, a Line tool for straight lines, a Rectangle for box shapes, a Circle for circles and ellipses, Text for text labels, and a Counter for numbered markers — useful for walking through steps in order. A Blur Box lets you redact content, an Eraser removes shapes, Select Color picks the stroke color, and Clear All wipes the canvas. Because these annotations are drawn directly over the app you are demonstrating rather than added later in an editor, the shapes and labels land exactly where the explanation happens. The Pointer toolkit makes your cursor impossible to ignore with four tools. Spotlight Mode (⌃S) dims the screen to black except for the area around your cursor, with feathered edges that keep it natural, so your audience stops looking at the wrong part of the screen. The Laser Pointer (⌃L) shows a vivid glowing dot that trails behind your cursor with a fading streak, with adjustable color, dot size, trail duration, and glow intensity — described as looking great in recordings and live screen shares. The Cursor Highlighter (⌃H) puts a bright ring around your cursor everywhere on screen, with customizable color, size, and shape to match your brand or mood. The Magnifier (⌃M) shows a circular magnified view that follows your cursor with a crosshair and adjustable zoom level, so you can show small UI details, tiny code diffs, or compressed content without zooming your entire display. Overall the product works as an overlay and a hotkey system rather than a recording suite. Capnot installs once, follows a guided setup, and settles into your menu bar. From there, every tool has a keyboard shortcut — no menus, no mode switching, no hunting for buttons — and captions and annotations appear on screen instantly. Because Capnot sits on top of any app, it works with any screen recorder or screen share; you are not locked into Capnot's own capture workflow. The site describes the whole path as three steps: install once, press a hotkey, and you're done, with no timeline editing and no post-production. A Settings window with four tabs — General, Caption, Annotation, and Pointer — lets you change every keyboard shortcut, font size, colors, corner radius, opacity, gradient borders, and more. The stated benefits follow from that design: you finish a walkthrough without losing another hour adding captions, arrows, and zooms in a video editor; you can explain what is on screen even in a noisy room, a muted call, or a silent social post; and viewers no longer miss the button, menu, or detail you meant to show because the pointer is impossible to lose. Because explanations go into the recording live, you record once and share immediately. Because Live Captions run on-device through macOS Speech Recognition, no audio leaves the Mac. And because it is a one-time purchase, there is no subscription hanging over every demo you make. Concrete use cases named on the site map to specific audiences. SaaS founders and indie developers ship product demos without voice-over, explaining a launch, feature, or code walkthrough with captions already on screen, then recording once and sharing immediately. Presenters keep their audience reading with on-screen cues, talking points, and live captions during conference talks and demos while their slides stay clean. Remote workers communicate without a mic by typing captions teammates can see during screen shares, which suits noisy environments, muted calls, or async video. Educators highlight what they are pointing at, annotate diagrams, and caption explanations in real time to make lessons easier to follow. Streamers drop captions, circle things on screen, and spotlight content live without touching their streaming software or switching scenes. On pricing and requirements, Capnot is $12.99 as a one-time purchase with one plan that includes every Caption, Annotation, and Pointer tool — no subscription and no feature-gated Pro tier. A 7-day free trial requires no card. The license covers one Mac, updates are included, and checkout is handled by Polar, with refund requests handled according to Polar's refund policy. Capnot supports macOS 13 (Ventura) and later, and Live Captions requires macOS Speech Recognition, available on Apple Silicon and Intel Macs. The app ships as a downloadable DMG, and the site notes there is no account creation, timeline editor, or complicated recording workflow. In short, Capnot's value proposition is that your next demo can explain itself: live captions, annotations, and pointer tools added to every recording on a Mac, for a single one-time price, with no voice-over, no editing afterwards, and no subscription.

Wonder Dev View is an IDE for engineers who care about design, built by Wonder and introduced by co-founder and CTO Boris. It is a development environment that combines multiple Claude Code and Codex accounts, each on its own subscription, with an editable design canvas that sits right beside your code and the running app. Wonder starts with the repository you already have: you can connect GitHub or open a local project, and agents work on your machine with your team's components, conventions and tools. Its stated purpose is to be a place to shape an idea, make it work and get it into someone's hands, giving engineers more room to use their own judgment. The product responds directly to how coding agents have changed development. In the launch letter, Boris writes that agents changed how much we can build, not how much we need to understand it, and that he wanted a development tool that keeps him close to what he is making. Two things he wanted in his own IDE shaped Wonder: multiple Claude Code and Codex accounts, each on its own subscription, and an editable design canvas right beside his code and running app. He describes wanting the design loop to feel ordinary, with fewer explanations about which button you meant and more time getting the button right. The underlying stance stated on the site is that good software still needs your judgment, and that a working result is where review begins. Wonder organises work around parallel worktrees. Each task gets its own worktree, with its chats, changes, terminal and pull request together in one place, so you can explore a feature while another agent fixes a bug, or start from an existing branch. For code-only work you can stay in the full-screen coding view. On the accounts side, you can connect several Claude Code and Codex accounts, each on its own subscription, pick the account, model and effort for each chat, and optionally let both agents share saved memory. The site notes that your agent subscriptions stay separate, so multiple accounts remain distinct rather than pooled. Context and autonomy are both configurable. You can give the agent the context you would give a teammate: a file, a design, a related chat or another worktree. You then choose how much it does on its own, from asking first to letting it run. In the walkthrough shown on the site, an agent given the instruction to redesign the pricing page reads the current plans and pricing table, edits the pricing table and plans files, searches for the price formatting helper, runs the pricing tests, reports that plans now carry a yearly price, and then continues with a monthly and annual toggle plus a Save 20% badge — with a note to ask Codex to review it when done. The design canvas is the second pillar. You can attach a screen from your designer and ask the agent to build it in your repo with your existing components, or bring a screen from the repo onto the canvas and work out the details visually. You can then run the app beside the design. Selecting an element in the Preview adds it to the chat so the agent knows exactly what you mean, and you can also capture an element onto the canvas, change it there and take it back to the repo. The stated aim is that this loop feels ordinary: fewer explanations about which button you meant, and more time getting the button right. Review and shipping are built into the same surface. Because a working result is where review begins, you read the diff, run your checks and can let Codex review Claude's code, or the other way around, while you still decide what is ready. From there you open the pull request, mark it ready, address feedback or fix failing checks, with the status shown right beside the changes — the walkthrough shows a draft pull request with the changed files, a Checks panel and a View on GitHub action. On Pro you can publish the app's Preview to a link someone can open in a browser and send it with the pull request. The canvas is collaborative too, with live editing on Pro, so a designer can shape the interaction while an engineer keeps it grounded in the code. Taken together, the approach is to keep agents, code, design and review in one continuous loop anchored to your own repository. Wonder does not ask you to move your project into a new hosted environment: it connects to GitHub or opens a local project, and agents work on your machine with your team's components, conventions and tools. Each task is isolated in its own worktree with its chats, changes, terminal and pull request together, so parallel agent work stays separated by task rather than tangled in a single working copy. Design context flows in both directions — a designer's screen comes into the repo as a build instruction, or the repo comes onto an editable canvas — and the running app sits beside the design so the element you point at is unambiguous. The stated benefits follow from that loop. Engineers get a development tool that keeps them close to what they are making rather than one step removed from it. Running several agents in parallel, each on its own subscription, lets you explore a feature while another agent fixes a bug. Giving the agent a file, design, chat or worktree as context reduces the back-and-forth about what you meant. Selecting elements in the Preview rather than describing them lowers the ambiguity in design feedback. Letting one model review the diff of another adds a second pass before you decide what is ready, and the pull request, checks and status sit beside the changes so shipping is not a separate context switch. Concrete workflows described on the site include adding a refund button to an order page, where the agent finds the refund logic in the payments service, reuses an existing ConfirmButton so it matches the rest of the page, edits four files and runs a typecheck; redesigning a pricing page with a monthly and annual toggle; exploring a design like a gallery card screen on the canvas; and reviewing a diff where Codex reviews Claude's code before the pull request is marked ready. Another workflow pairs a designer shaping an interaction live on the canvas with an engineer keeping it grounded in the code, and a further one publishes a working Preview link so someone else can try the app and receive it alongside the pull request. Wonder runs on Mac and Windows and is free to start, with downloads offered for macOS. Shared canvas editing and Preview links are on Pro, while your agent subscriptions stay separate. The site lists engineers at Autentic, Deeploi, Arc, Browserbase and CodeRabbit as users. It is aimed at engineers who build with coding agents and care about design, and at teams where designers and engineers work on the same interface. Full documentation for the design features is available in the Wonder docs. The takeaway is that Wonder Dev View is a place to shape an idea, make it work and get it into someone's hands, built on the belief that agents change how much we can build but not how much we need to understand it. By putting multiple Claude Code and Codex accounts, parallel worktrees, an editable design canvas and a running app preview side by side — and closing the loop with cross-agent review and pull requests — it gives engineers more room to use their judgment from the first prompt to the pull request.

Dessn is a design canvas that runs your actual production codebase. Rather than working from a static mockup file, you connect an approved repository and Dessn pulls your real product pages onto a canvas where you can view every page and state, explore ideas, prototype, review, and QA with your team. It is built for product teams with an existing codebase — designers, PMs, and engineers — who want to explore and collaborate on product ideas directly in production code without opening an IDE. The stated purpose is simple: see what's live, and fix what's drifting. Dessn describes itself as sitting at the intersection of design and dev tools, with a canvas purposely built for design workflows while remaining anchored in production. The problem Dessn addresses is one that appears when product teams ship fast with AI: it becomes hard to keep track of what is actually live and hard to keep the product consistent. Design artifacts drift away from what is really shipped, and there is no shared source of truth for humans and agents to work from. Dessn's answer is to run your codebase on a design canvas that is always in sync with prod, so your team and your AI agents work from one shared source of truth. One customer quote on the site captures the reasoning directly: production code is the source of truth for design. Another frames the appeal for designers as being able to explore ideas in the context of your production codebase. The first capability group is visibility into what's live. Dessn shows every page as it exists in production, lets you visualize entire user flows, and stays always in sync with prod. On the site this is illustrated with a fictional banking app called Orbit, where pages such as /cards, /cards/freeze, /cards/replace, /cards?frozen, and /cards/replace/done are laid out as states of a single flow. A second example walks through a meeting-booking flow from an event type to a confirmed booking, on prod data. Seeing flows as they actually exist lets a team review the real product surface — including edge states like a frozen card or a completed replacement order — instead of guessing from a stale file. The second capability group is consistency. Dessn lets you audit your entire product in one prompt, automate your design system, and iterate with perfect fidelity to prod. The site shows an audit thread titled "Tables drift across 4 pages," which reports that 12 tables were audited in a product called Fern and that two patterns were found with four differences — header capitalization versus sentence case, amount right versus left aligned, row height of 40px versus 56px, and status shown as a badge versus plain text. This illustrates how the audit surfaces concrete, comparable inconsistencies across pages so the team can decide what to standardize. Combined with a design system that stays in sync and full fidelity to production, the team iterates on the real components rather than a simulation of them. The third capability group is a design-first environment for collaboration. Dessn offers direct editing, sharing and collaboration, and what it calls the best design agent. Teams can comment, branch, and iterate together, and share prototypes for reviews, user tests, or sales demos. A comments example on the site shows a thread on a finance app screen where a teammate asks whether to show which account pays each bill. Dessn also ships an MCP that lets it work inside the AI you already use: you can view your canvases and prototypes plus the code behind them, create new flowcharts, prototypes, or comments, and approve Dessn's work from your terminal, switching to Dessn when you want to polish by hand. The MCP install page shows logos for coding agents including Claude Code, Cursor, Codex, and Grok, and the site offers a one-line prompt to paste into a coding agent to set up Dessn for a project. How it works is described in three steps. First, you connect your repo in one click — the site's example is Connect GitHub. Second, Dessn compiles your codebase, a step the site describes as taking 30 minutes while it sets up your design environment. Third, you pull your product pages onto a canvas to design, review, and QA with your team. In the FAQ, Dessn adds that there is no lengthy implementation: connect an approved repository and your first page is usually ready in minutes, because Dessn learns the existing routes, components, and design system automatically. It also states that you can design and prototype in your own codebase without coding required, and that it requests read-only repository access, never accesses backends, databases, or customer records, and never writes, modifies, or pushes code back to your repository. The benefits that follow from the content are straightforward. Teams get visibility into what is actually live instead of an approximation of it. They get a shortcut to consistency, because auditing and design-system work happen against production rather than a separate source. They get design work with perfect fidelity to the real design system, which means what they explore is what ships. Collaboration improves because the canvas is shareable and commentable, and prototypes can be reused for reviews, user tests, and sales demos without rebuilding them. And because agents can operate through Dessn's MCP, human and AI work converge on the same canvases and prototypes rather than diverging. Concrete use cases described on the site include auditing a product to find inconsistencies, mapping user flows with states and personas, commenting, branching, and iterating together, and sharing prototypes for reviews, user tests, or sales demos. The illustrated examples — a card-freeze and card-replacement flow, a booking flow from event type to confirmation, a cross-page table audit, and a commented finance dashboard — show what that looks like in practice across multi-state flows and shared design review. Teams that need to QA pages against production, or want a demo environment built from real product screens, are described as working in this same canvas. Dessn is explicitly for product teams with an existing codebase: designers, PMs, and engineers. It supports React, Vue.js, Electron, and React Native apps, and you connect your codebase through GitHub or GitLab. Access can be restricted to specific repositories or projects, and for monorepos Dessn can be configured to work from the relevant project or subdirectory, with access revocable at any time. Dessn states that it does not train on your data and uses LLM providers that guarantee no training and zero data retention, and that it is fully SOC 2 Type II compliant. No credit card is required to get started. Named customers and users shown on the site include Mercury, Vidsy, Wispr, Greenlight, Color, Noyo, ngrok, Lette, Great Question, Operate, Pencil, Ethena, Planned, Raylu, Seapoint, Groom Club, Red Rover, Dataflo, Comfy, Flowglad, Ascend, and Startale, alongside testimonials from designers and product leaders describing the product as a way to work inside production code. In short, Dessn's value proposition is that design, review, and QA happen on the code that actually ships. By turning your production codebase into a collaborative design canvas that stays in sync with prod, it gives teams and their AI agents one shared source of truth — so they can see what's live, fix what's drifting, and move from idea to merge-ready product work in the same place.

Clayzo is an animation engine built for coding agents. Rather than building motion design by hand in a separate animation tool, you describe the motion you want and an agent such as Claude Code, Codex or Cursor writes it in code, looks at the result, and iterates. The finished animation can be shipped as a video or played live on your website. Clayzo presents itself as animation built for Claude Code, Codex and Cursor, and its showcase spans campaigns, 3D, product UI and interactive pieces, from a ten-second feature launch post to a cursor-driven halftone portrait. That range makes the product relevant to anyone who already works with a coding agent and wants motion graphics to come out of that same workflow instead of a separate pipeline. The idea behind Clayzo is a shift in how motion design gets made. Coding agents like Claude Code, Codex and Cursor already work inside a code workflow, and Clayzo gives them an animation engine to work with, so motion becomes something the agent produces rather than something assembled by hand frame by frame elsewhere. The site states the loop plainly: you describe the motion, your agent writes it, looks at the result, and iterates. The output is either a video you ship or an animation that plays live on your site, so one piece of work can serve a launch video and an interactive page. Everything you make is yours to reuse and remix, and Clayzo says work is never locked in: you can easily change anything or spin up as many variations as you like. The product is backed by Afore Capital and is free. Clayzo is designed to work with coding agents rather than replace them. The site names Claude Code, Codex and Cursor specifically, and its own FAQ asks which agents it works with and which models work best, making agent compatibility a first-class part of the product. In practice this means the animation is authored in code by the agent you already use, inside the environment you already work in, so there is no separate animation editor to learn. You describe the motion in plain terms, the agent writes it, and Clayzo provides the engine the agent targets. For teams already directing agents at other development tasks, motion graphics become one more thing the same agent can be asked to produce, keeping animation inside the existing coding workflow instead of splitting it off into a different tool and a different set of skills. A defining part of the workflow is the review loop. Clayzo's pitch is that your agent writes the animation and then reviews every frame, iterating until the motion matches what you described. That feedback cycle is what makes an agent-authored animation practical: instead of accepting whatever code comes out first, the agent can inspect the rendered result and refine it. When the animation is right, you choose the output. You can ship it as a video, exporting it as motion graphics for use wherever a video file is needed, or you can play it live on your site as a running animation. The same underlying work therefore covers both a rendered clip and an in-browser experience, which is useful for launch posts, ads, logo stings and product explainers that often need to exist in more than one format. Clayzo also covers live and interactive animation on the web. Beyond video, animations can play live on your site, and the showcase includes interactive pieces whose motion responds to the visitor: a halftone portrait rendered as a screen with a lens that follows the cursor and reveals the photograph underneath, a field of split discs that turn to face the cursor and flare hot where it passes, with a click sending a ripple through them, and a premium card that tilts toward the cursor with a highlight sliding across the metal and a shadow that follows. Other examples include a settings toggle, an agent thinking state, a payment confirmation and a piece that follows your cursor, showing that interaction and micro-motion are part of what the engine is built to produce. Clayzo's player is presented as lightweight. The site includes a direct comparison of WebGL players and states that Clayzo is 1.0× smaller than Rive, showing the Clayzo WebGL player against the Rive WebGL player. A smaller player matters for live animations because the runtime is what every visitor loads when the animation plays on your site, so keeping it small supports the goal of playing motion live rather than only embedding pre-rendered video. Getting started is similarly lightweight: the site shows a single command, npx clayzo@latest init, which is how you initialize Clayzo in a project. From there the work happens through your coding agent, which writes the animation in code and iterates against the result, with Clayzo supplying the engine and the WebGL player that ultimately runs it. Overall, Clayzo's approach is to treat animation as code written and refined by an agent. You describe the motion; the agent writes it, looks at the result, and iterates; the output ships as a video or plays live on a site. Because the work is code rather than a locked file inside a proprietary editor, everything you make is yours to reuse and remix, and the site emphasizes that it is never locked in: you can easily change anything or spin up as many variations as you like. The showcase illustrates the breadth of that approach across campaigns, 3D, product UI and interactive categories, covering launch posts, ads, logo stings, 3D shorts, explainers, product demos and cursor-driven web interactions, all produced through the same agent-driven loop. The benefits follow from that loop. Because the agent reviews every frame and iterates, you can refine motion by describing what you want rather than by manually adjusting it. Because the result can be exported as video or played live, you avoid rebuilding an animation for each destination. Because the player is WebGL and described as smaller than Rive's, live playback stays lean. Because everything is yours to reuse and remix and is never locked in, animations can be changed, extended and turned into multiple variations without starting over. And because Clayzo works with the coding agents teams already use, motion graphics fit into the existing workflow. The product is free, which removes cost as a barrier to trying the agent-driven approach on a real project. The showcase lays out concrete use cases. Campaign work includes a launch post for a Stack feature, where loose shapes snap into an auto-layout grid and teammates resize and duplicate them live. Advertising includes a punchy square ad for three new flavours, with each can flooding in on its own colour and a stamp that keeps turning. Branding includes logo stings: four quarter tiles swinging in and locking into a mark for Quarto, and a single white stroke on red drawing a crescent and writing a wordmark for lune. 3D work includes a wooden marionette short and a 3D explainer of Intel's earnings with a chip riding a rollercoaster. Product UI work includes a running app product shot where a tap opens into detail with the route replaying and splits rolling in, an AI spreadsheet where a Funding stage column fills in and grows into a chart, and an issue tracker where a new issue lands on the board and is dragged to In Progress. Interactive work includes the halftone lens, pointer field and tilt card. Clayzo is aimed at people who work with coding agents such as Claude Code, Codex and Cursor and want to produce motion graphics, product animations and interactive web animations through them. Its showcase categories, campaigns, 3D, product UI and interactive, indicate the kinds of work it is used for, from marketing launch posts and ads to product demos and cursor-responsive web experiences. The site notes that Clayzo is backed by Afore Capital and states that it is free, with a getting-started step of running npx clayzo@latest init. Its FAQ addresses questions about what Clayzo is, how to get started, which models work best, whether you can use your own images, fonts or components, which agents it works with, whether it is really free, where you can play Clayzo animations, and how it differs from Rive or Lottie. Clayzo's core value proposition is simple: animation, built for coding agents. You describe the motion, your agent writes it and reviews every frame, and you ship the result as a video or play it live on your site through a WebGL player the site describes as smaller than Rive's. Everything you make is yours to reuse and remix, nothing is locked in, and variations are easy to spin up, all through agents you already use and at no cost.

magpie is a visual library for everything you save online. It pulls your saves from Instagram, X, TikTok, Reddit, Pinterest, Cosmos, Are.na, GitHub, YouTube, Dribbble, Behance and your browser's bookmarks into a single place, then uses AI to tag them so you can find them by what's in them, or by colour. Its stated purpose is simple and direct: everything you've saved, in one visual library, so that the references you collect while browsing can actually be found again later instead of disappearing into separate apps and feeds. The problem magpie is built around is one most people who collect visual references know well. Saves end up scattered across many platforms, each with its own bookmarking gesture and its own search that rarely works on images. What you saved to Instagram lives in Instagram, what you pinned lives in Pinterest, what you bookmarked in your browser is buried in a bookmarks list, and none of those places can answer a question like "that photo of somewhere by the sea." On top of that, posts get deleted, accounts go private and content vanishes, which means the references people rely on for work can quietly disappear. magpie addresses both halves of that problem: it consolidates the sources and it keeps the save even when the post is gone. At the centre of the product is the unified library. The site describes saving from Instagram, X, TikTok, Reddit, Pinterest, Cosmos, Are.na, GitHub and your browser's bookmarks, with Chrome, Safari and Firefox represented alongside those sources, and the Product Hunt listing summarises it as saving from Instagram, X, TikTok, Pinterest, YouTube, Reddit, Dribbble, Behance, Are.na and the web. Rather than visiting each network to remember what you liked there, the library presents everything as a single visual collection. In the sample library shown on the site, that collection holds 265 saves consisting largely of design references: identity work, packaging, posters, typography specimens, photography and motion pieces, each labelled with the domain it came from. AI tagging is what makes that single collection usable. Every save is tagged by AI so that you can find saves by what's in them, rather than by whatever caption or title the original post happened to have. The site demonstrates this with a search for somewhere by the sea, which surfaces seaside imagery, and with a view of the saves that are mostly one of its blues, showing how colour functions as a first-class way to filter a library. Colour search is described as a way to find saves by colour, and it is presented next to meaning-based search as a paired way of navigating a large visual collection. A second search layer is the bar that sits over the library, labelled "Find, or ask for references." It can be reached with ⌘K, and the site notes that you can paste a link anywhere to add something new. Beyond plain lookup, the bar accepts a request in natural language: the demo types in references for a calm hardware launch. In response, magpie picks six saves from the library, gives a reason for each pick, and makes a project of them. That turns a passive archive into something you can interrogate, where the library proposes material from what you have already saved and explains why each piece fits the brief you typed. Browsing the library itself is supported by several views. The interface shown on the site includes Rows, Cells, Grid and Canvas as ways of arranging the same set of saves, with a control for zooming the grid in and out. Individual saves can be opened: the demo shows a video save opening and playing inside the library, and an Instagram collection of posters and prints being viewed as a group. Because saves are kept even when the original post is gone, the copy in the library remains available after the source disappears, which is the core archival promise of the product. The overall approach combines capture, automatic organisation and retrieval. Capture happens by pasting a link anywhere or by bringing in saves from your connected sources, including your browser's bookmarks. Organisation happens automatically through AI tagging, so nothing depends on you filing things correctly at the moment you save them. Retrieval then happens through three routes that the content demonstrates: searching by meaning, searching by colour, and asking for references in plain language and receiving a curated project with reasons attached. The result is a library that behaves like a visual memory rather than a folder tree. The stated benefits follow from those mechanics. You get one place for everything you save instead of many; you can find an image by describing it or by its colour rather than trying to recall its caption; and your saves survive deletion of the original post. For people whose work depends on accumulated visual references, the demo's calm hardware launch request illustrates the practical payoff: instead of scrolling back through months of saves, you type the brief and receive six relevant pieces with an explanation of why each one is there, already grouped into a project you can work from. Concrete use cases visible in the content include searching a library for somewhere by the sea; opening and playing a saved video directly in the library; filtering to the saves that are mostly a particular blue; browsing an Instagram collection of posters and prints as a set; and asking for references for a calm hardware launch, where magpie assembles six annotated saves into a project. The sample library itself is full of design material such as brand identities, packaging, type specimens, campaign films and photography, which reflects the kind of reference gathering the product is demonstrated with. magpie is available as a Mac download and through the web, where you can start for free. The Product Hunt listing files it under Chrome Extensions, Design Tools and Productivity, and the site's source list spans social and visual platforms alongside browser bookmarks. Its natural audience is people who collect and reuse visual references, particularly designers working with identity, packaging, typography, photography and motion, who need their saved inspiration to be searchable, colour-aware and permanent. The takeaway is straightforward: magpie turns scattered saves into one visual library that is tagged by AI, searchable by meaning and colour, answerable in plain language, and kept even when the post is gone.

ilse is a visual feedback layer for AI code agents. It puts a toolbar on top of the React app you already have running in your browser, letting you click an element, write a note, or drag, resize, and tweak things directly on the live interface. Those instructions then reach the coding agent you already use — Claude Code, Codex, Cursor CLI, Gemini CLI, or any agent that speaks MCP — already pointing at the right spot in your code. ilse is built for developers and designers whose app already exists and who want to refine its interface without leaving it, on their own machine, using their project's own tokens and components. The problem ilse addresses is captured in the maker's own story. He had been a designer for over nine years and found himself spending more time in code than in Figma. He would ask Claude Code to fix a button, describe in words what he saw on screen, and the agent would edit the wrong file. As he puts it, the agent writes well, but it does not see what he sees — it was missing a designer's eye. So ilse was built so he could point at the real app, let ilse gather the context, and have the agent change the code. The site draws an explicit line between ilse and generators such as Lovable, Claude Design, and Replit: those are described as great for starting from scratch in their own environment, while ilse is for when the code already exists and lives on your machine. The core of the product is Design Mode. You click an element or select some text and write what should change. The note reaches your agent already pointing at the right spot in the code, so there is no loose "crooked button" description to interpret. That is the point-and-say workflow: the instruction is anchored to a specific element in a specific file rather than to a vague verbal description of a screen. The panel extends that same idea with gestures. Move, resize, edit text, and choose whether a change applies only to this instance or to all of them. There is also a Draw control and Screens, which is marked as new. Drag lets you reorder elements, and you can tweak spacing and colors using your project's own tokens. Simple tweaks land instantly with no AI involved, spending few or zero tokens, while anything that goes beyond a class mapping is handed to your agent to change in the code. A component library is listed as a Pro capability. Before calling your agent, ilse does the search for it. What the agent receives includes the element's file and line with only the snippet that matters, the component that wrote the JSX and where it is used, your project's design tokens, your note plus the panel tweaks and gestures you made, what a test found (the journey step, the screen, and the element), and any pasted references you optionally add. This context packaging is what replaces the agent guessing at which file to edit. Test Mode, available in Pro, is the second major capability. You describe what a person does — the site's example is "buy the lamp" — and ilse walks the flow in the real app on its own, then shows where it got stuck and reports what breaks. Pro is described as test mode with accessibility, errors, layout, the memory, and journeys, plus the Library's hands: dragging components onto the page, their copies, and taking your design system to other tools. A Review Mode is listed as coming soon: reviewers would leave notes on your product's own link without needing an account, and those notes would reach both you and the agent so the loop starts again. The overall workflow is deliberately lightweight. You run npx ilse-design@latest in your project folder; Next.js, Vite, CRA, Remix, and Astro are detected on its own; the app opens at the usual address with the bar on top. There is no extension, no app, and no account. ilse runs on your machine as a CLI and an MCP server, and the agent you already pay for is the one that changes the code. The loop is refine, test, and repeat. Privacy is a structural part of the design rather than an add-on. Everything runs on your computer and ilse has no server to send your code to. The code, the context, and the history stay local. ilse never touches a key — your agent is the one calling the AI. And the bar only exists on the dev server, so the final production build ships nothing. ilse describes itself as an agentic WYSIWYG rather than a faster generator, and it does not generate anything from scratch. The site's comparison table frames the difference: generators start from a blank prompt, while ilse starts from the app that already runs; generators produce code generated anew, while ilse edits your repo in place; generators apply whatever design system the tool picks, while ilse uses your tokens and components; and generators use the platform's AI on its credits, while ilse uses the agent you already use. A testimonial on the page from Huxley Dias of Punk Metrics states that if you develop locally, this is excellent for refining interfaces, and calls it way better than Claude Design, Replit, and Lovable. The benefits follow directly from that approach. Because simple tweaks map to a class and land instantly, common adjustments cost few or zero tokens. Because the note arrives with the file, the line, and the component context attached, the agent no longer edits the wrong file. Because your own tokens and components are part of the context, refinements stay inside your design system instead of introducing a parallel one. And because everything runs locally against your dev server, your code and history never leave your machine. Concrete use cases described on the site include clicking any element in a demo shop interface to change it, reordering items by dragging, resizing elements, editing text in place, tweaking spacing and colors against your tokens, and pasting optional references into the note. Test Mode covers journeys such as buying a lamp, where ilse walks the flow and identifies the step, screen, and element where it got stuck. Design Mode covers the everyday case of pointing at a crooked pixel and telling the agent what to do about it. ilse works with any React project that has a dev server. The listed frameworks are Next.js, Vite, Remix, Astro, and React, with CRA mentioned as auto-detected. Styling and component systems listed are Tailwind, shadcn/ui, Radix, and MUI, and the project's design tokens and components go into the context. Supported agents include Claude Code, Codex, Cursor CLI, Gemini CLI, Cline, Windsurf, GitHub Copilot, Goose, and any agent with MCP. Pricing is free for the core tool under the PolyForm Shield license, which allows free use including at work as long as you do not turn it into a competing product. A founder pre-sale offers Pro at US$49 instead of US$79 as a one-time payment, limited to the first 10 founders, with Pro shipping by November 30, 2026, a refund if it does not, renewals locked at the founder price, a roadmap vote, a direct channel with early builds, and a 30-minute 1:1 setup. The Pro purchase includes 12 months of updates counted from Pro's launch, after which the version you have stays yours. In short, ilse's value proposition is precision: you show the agent what you mean by pointing at the real running app, ilse gathers the code context so the instruction lands in the right place, and your existing agent makes the change in your repo with your tokens — all locally, with nothing shipped to production.

JST-2 is Viggle's physics-native world foundation model for generating 3D scenes, characters and full-body motion in one shared world. Built by Viggle, which describes itself as a spatial GenAI platform, the model is centered on characters: it takes one image, or text, and returns a complete full-body 3D character with motion dynamics already built in, ready to animate without separate rigging and skinning steps. The same model generates 3D scenes, which Viggle says take about one second, and produces motion from text or video. It can be tried free in the PINOC browser app, used through Viggle's API, or accessed via MCP with agents such as Claude Code. The character research page documents how the model was tested and where it still falls short. In a conventional animation or game pipeline, a generated character is only the starting point. Rigging and skinning are specialist work measured in hours, and once a rig is built it decides how the character moves for the rest of its life. JST-2 targets exactly that handoff: instead of building a shape and then attaching a rig, it builds the character and its motion dynamics in one generation, so a single input image or prompt yields something already animation-ready. A second problem is input quality. Real creative inputs are not studio plates, and lighting, angle, framing and occlusion are all over the place. Characters are often cropped, turned away, or missing hands and feet, and the model is designed to complete those partial inputs into a body whose structure and proportions hold together. JST-2 generates 3D characters from a single image or from text. In Viggle's blind human tests against Meshy 7.1 and Tripo H3.1, the model led both baselines on character generation, with its strongest results on facial detail. Face quality scored 63.0% against Meshy 7.1 and 82.0% against Tripo H3.1, where a score of 50% means equal preference and draws count as half a win. Facial detail is what preserves a character's identity in 3D, which matters when the asset is your own digital double, an actor's stand-in, or an IP character you will reuse for years. Texture quality, body quality and clothing quality were also rated above both baselines: against Meshy 7.1 the model averaged 58.4% overall, and against Tripo H3.1 it averaged 73.2%. The model also completes characters from cropped or partially occluded images across a wide range of visual styles. When only the upper body is in frame, when the subject is turned away, or when hands and feet are hidden, JST-2 extends the visible region into a full body whose structure and proportions hold together. Viggle notes that on clean images all three tested models are usable and the gap opens on difficult ones, and that real creative work rarely starts from a clean image. The test set reflects this: alongside the photoreal, stylized, cartoon, anime and sci-fi categories used by major commercial 3D asset platforms, it adds hard samples drawn from what users actually upload, including unusual poses, occluded subjects, off-axis viewpoints, bodies cropped out of frame, difficult lighting, low-resolution and blurred images, and busy backgrounds. Motion is where the model is most differentiated. On animation readiness, defined as how a generated character moves once it is driven, JST-2 led Meshy 7.1 with an average score of 73.8% and Tripo H3.1 with 88.9%. The criteria were joint alignment, rig usability, deformation quality and hand rigging. Hand rigging reached 82.8% against Meshy 7.1 and 89.3% against Tripo H3.1, while deformation quality reached 77.6% and 93.2% respectively. Every finger moves on its own, and once the character moves, clothing, hair and accessories deform with the body rather than fighting it. All of this happens in a single generation: complete body, 3D character and motion dynamics at once, with no remesh and no separate rigging step left for the user. Under the hood, JST stands for Joint Space-and-Time. The model represents the world as persistent atoms in 3D space: each atom represents a piece of matter or an object part and retains its identity as it moves and deforms, carrying an explicit xyz position plus learned vectors for its attributes and dynamics. These atoms are encoded as Physics-Native Tokens, and the JST decoder turns them into 3D Gaussian splats. A JST-2 character is built from these tokens, and the dynamics vectors inside its Physics-Native Tokens encode how each part deforms under different motions. Because those dynamics are learned from data rather than authored by hand, the character arrives ready to animate without separate rigging and skinning steps. The practical benefit is removing a specialist bottleneck. Rigging and skinning normally take hours of expert work, and the resulting rig locks in how the character can move for its whole life. With JST-2 the character and its motion dynamics come out of one generation, so teams can go from a single image or prompt to something they can drive. Identity is preserved in 3D through stronger facial detail, which helps when the same character is reused across projects. Because the model handles cropped, occluded and awkwardly framed inputs, teams can work from material they already have rather than requiring studio-grade photography. And since scenes, characters and motion are generated in one shared world, the outputs are meant to fit together rather than being assembled from separate tools. Use cases follow directly from the benchmarks. Viggle names digital doubles, actor stand-ins and IP characters that will be reused for years as the kinds of assets where preserved facial detail matters. Game and animation pipelines benefit from characters that arrive without a rigging step, with hands that articulate finger by finger and clothing that deforms with the body. Teams working from user-uploaded or imperfect imagery can complete partially framed subjects into full bodies. Beyond characters, the model generates 3D scenes in about one second and produces motion from text or video, and it can be driven through an API or through MCP with agents like Claude Code, which supports automated and agent-based workflows. The product is aimed at people building 3D characters and scenes: character artists, animators, game and animation production teams, and developers who want to call the model programmatically. Access options named in the content are the PINOC browser app, where it can be tried free, the Viggle API with a developer portal and documentation, and MCP for use with agents such as Claude Code. Viggle also lists related products including Meme Maker, PINOC, Games and the API. The technical foundation described is the Joint Space-and-Time representation, Physics-Native Tokens and 3D Gaussian splats, with learned dynamics driving character motion. Viggle is explicit about current limits. Gaussian artefacts can appear around limb joints, and in some cases the shoulder-to-torso join, the armpit and the gaps between fingers show holes, merging or collapse, which the page calls the most visible failure mode. Surface detail has a ceiling: hair, clothing and materials read cleanly at normal viewing distance but soften as the camera moves in. Unusual bodies that depart from normal human proportions, such as very long limbs, an exaggerated head-to-body ratio or a non-humanoid silhouette, can produce off joint placement and deformation. Clothing, hair and accessories deform with the body but are not simulated, so they do not swing from inertia or collide with the body or each other; scene collisions and object interactions still rely on rule-based physics. Viggle says upcoming releases will improve how complex clothing deforms during motion and how hair moves, and that JST-3 is in development to replace those rules with learned dynamics. In summary, JST-2's value proposition is a single model that produces complete, animation-ready 3D characters from one image or text, with motion dynamics built in rather than rigged afterwards, backed by Viggle's own blind human benchmarks against Meshy 7.1 and Tripo H3.1 and extended by scene generation and text- or video-driven motion in one shared world.

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SurgePix is an AI visual generator combined with a layer-based editor. The product's own description states that it generates images, decks, carousels and posters with AI, and then lets you edit every layer and export a finished, ready-to-publish result. The website frames the experience in three moves — dream it, edit it, call it. In practice that means a user can start from an idea, turn it into a visual with AI, refine that visual on an editable canvas, and either publish the result or reach the same capability from an external agent workflow. SurgePix is therefore aimed at anyone who wants AI-generated visuals but does not want to be stuck with a single, unchangeable output, as well as at teams that need their visuals to stay on-brand. The problem SurgePix addresses is stated directly in the product's own summary: most AI image tools stop at one-shot generation. A prompt produces a picture, and that picture is essentially final. If a word is wrong, a region does not fit, or the result drifts from the brand, the usual remedy is to regenerate the whole image and hope for a better outcome. That makes AI-generated visuals awkward to use in real publishing work, where an asset usually has to be corrected, adapted and matched to existing brand standards before it can go out. SurgePix is positioned as the step after generation — the part where a visual becomes a finished, publishable asset rather than a promising draft. SurgePix generates four kinds of visual output as named on the website: images, decks, carousels and posters. Covering several formats in one tool matters because these are the shapes that finished visual work usually takes — a standalone image, a multi-slide deck, a sequence of carousel panels, or a single large-format poster. Instead of treating AI generation as a one-off image experiment, SurgePix applies it to formats that are actually published. Each of these can be produced with AI and then moved onto the editable canvas, where the second half of the workflow — editing layer by layer — begins. The layer-based editor is the core of the product. SurgePix keeps a generated visual editable, so you can edit any layer rather than working on a flattened picture. That single design decision unlocks the specific actions the product advertises: swapping text inside the design, repainting a region, and adjusting individual elements while the rest of the composition stays as it was. For a user, this changes the nature of AI generation. The first output is a starting point rather than a final answer, and corrections are targeted instead of wholesale. When a headline needs rewording, you swap the text; when part of the image is wrong, you repaint that region. The rest of the visual is preserved. SurgePix also includes a brand kit, which the product describes as the way to stay on-brand. Brand consistency is usually the point where AI-generated visuals break down: the image looks good in isolation but does not match the brand's established look. By keeping brand assets in a kit and using them while editing, SurgePix makes it possible to bring a generated visual into line with existing standards instead of rebuilding it. Combined with layer editing and text swapping, the brand kit supports a workflow where AI supplies the composition and the user enforces the identity before anything is exported. The workflow closes with export. SurgePix's promise is a finished, ready-to-publish result — not a draft that still has to be taken into another design tool for cleanup. Once the layers have been edited, the text swapped, regions repainted and the brand kit applied, the visual leaves the product in a publishable state. That end-to-end framing is what distinguishes the product's positioning: generation, editing and export are presented as one continuous path, and the layer editor is the bridge between the AI output at the start and the finished asset at the end. The second route into the product is programmatic. SurgePix describes itself as agent-callable and offers open-source agent skills that let Claude Code and Cursor generate and edit visuals directly. The website puts it as calling SurgePix directly from your agent workflow, alongside the alternative of doing the work on the editable canvas. For developers, this means the same generation and editing capability can be invoked from inside the tools they already use, without switching to a separate interface. The product explicitly frames this as a way to skip the UI when that is preferable, and the skills are described as open-source. SurgePix also refers to a Designer Agent that turns ideas into visuals, and to an editable canvas where users make those visuals their own. The site's three-part summary — dream it, edit it, call it — maps onto this: the agent produces the initial visual, the canvas is where it is refined, and the call route is where the capability is reached from outside. Users are not required to pick one path permanently; the same visual work can begin inside the product or be requested from an agent, depending on how the person prefers to work. The practical benefit is control after generation. Because every layer remains editable, users can correct AI output instead of accepting or discarding it, and can adapt one visual direction to different messages by swapping text rather than starting from a new prompt. Region repaint keeps fixes local, so a single flawed area does not cost the whole composition. The brand kit addresses consistency, and export addresses completion. Taken together, these features are aimed at shortening the distance between an AI-generated idea and something that can actually be published — the gap the product says most AI image tools leave open. Concrete uses follow from the formats the product names. A carousel can be generated and then edited, with text swapped and regions repainted until the content reads correctly. A deck can start from AI-generated visuals and be refined on the canvas before export. A poster can be produced, brought in line with the brand kit, and exported as a finished asset. Teams that need on-brand output can apply the brand kit during editing rather than rebuilding assets. Developers can generate and edit visuals from Claude Code or Cursor through the open-source agent skills, keeping visual production inside their agent workflow. SurgePix is delivered as a web product — the site offers an entry point into the tool and an editable canvas — and it is also reachable from agent workflows, which is where the Claude Code and Cursor skills come in. Its stated audience skews toward people producing visual content: designers and creators working with images, decks, carousels and posters, and teams that care about brand consistency. The agent-callable route extends that audience to developers and technical users who would rather invoke the capability from their own environment than work inside a separate interface. The consistent theme across the product's own description is continuity. SurgePix does not present AI generation as the end of the job; it presents it as the beginning of an editing job that finishes with an exported, publishable visual. Images, decks, carousels and posters are generated with AI, every layer stays editable, text can be swapped, regions can be repainted, the brand kit keeps output on-brand, and the finished result is ready to publish. Users who prefer to work outside the interface can call SurgePix from Claude Code or Cursor through open-source agent skills. That combination — a real editor on top of AI generation, plus an agent-callable path — is the product's stated value proposition.

Penta is a CD player app for iPhone and iPad that turns your Apple Music library into a five-disc CD changer. Rather than presenting your albums as a list, it recreates a '90s five-disc carousel: you pick an album, open its jewel case and drop the disc into the carousel, then play music the old-fashioned way with authentic sights and sounds. It is built for people who want the ritual of physical media back on a device they already carry, and it is free on iPhone and iPad. Penta 1.3 extends the idea to iPhone Duo, where the phone itself becomes the jewel case. Streaming libraries made music instant and invisible. Everything you own sits behind a search field, and the small ceremonies that used to surround listening — pulling a case off a shelf, reading the liner notes, hearing a tray motor slide out — disappeared along with the hardware. Penta's answer is to rebuild those ceremonies in software. Its starting point is the five-disc CD changer, a piece of '90s hardware that held a fixed set of albums and asked you to swap discs as you went. The app keeps that constraint and the physicality around it, so browsing a library becomes a sequence of small physical actions again instead of an endless scroll. The core of Penta is the carousel. Five discs sit on it beside a glowing display, and loading one follows the same order every time: pick an album, open the jewel case, drop the disc into the carousel. Music plays the old-fashioned way, from the carousel rather than a queue. Every disc carries real album art, so the changer looks like a rack of CDs rather than a grid of thumbnails, and an optional one-time Pro unlock adds the real printed CD art. Because only five slots exist, choosing what goes in becomes part of using the app — the same small decision a physical changer forced on its owner. Penta's sound design is the detail that sells the illusion. Every click, whir and tray motor in the app was recorded from a real CD changer, so the noises that accompany a tray opening or a disc being loaded are sampled from physical hardware rather than synthesized. Paired with the authentic sights of the changer, those sounds make loading an album feel like operating a machine. The glowing display and its level meters are decorative: Apple Music audio is protected, so no app can read the live signal, and Penta states that the meter animation is decorative by design. iPhone Duo support, arriving with Penta 1.3 as a free update, is where the concept gets unusual. On iPhone Duo the phone itself is the jewel case. Closed, you are holding the album; opening the Duo opens the case with it, because the hinge is the case's hinge — liner notes on one side, the disc on the other. Opened flat, the whole deck is visible: five discs on the carousel and a glowing display. Folded like a laptop, the layout becomes a CD wallet above the deck, so you can flip through your collection above the tray. Held upright, the app shows a CD wallet over the tray that animates open and closed. On iPhone today you also get keys you can feel, and on iPad the app works any way up. Underneath the hardware impression, Penta works with the music you already have in Apple Music. It reads your library to show and play albums and never adds, removes or edits anything. Albums you own play without a subscription; streaming from the Apple Music catalog requires an Apple Music subscription. The app needs permission to access your music library — if access was declined, it can be turned back on in Settings › Penta › Media & Apple Music — and catalog albums will not play without a subscription either. Penta runs on an iPhone or iPad with iOS or iPadOS 17 or later. The benefit is atmosphere: the same library, heard through a deliberately limited, tactile interface. Because the carousel holds five discs, listening becomes a short set of deliberate choices rather than an infinite queue, and because every interaction carries recorded mechanical sound and real album art, the app restores some of the weight that digital music lost. It also respects what you already have — the app is free, an optional one-time Pro unlock adds the real printed CD art, and nothing in your library is modified by using it. In practice, Penta suits a few clear situations. It is for the listener who wants to load a handful of albums and let the changer run. It is for anyone who used a '90s changer and wants the pick, open and drop sequence back, complete with recorded clicks and whirs. It is for iPhone Duo owners who want to close the phone and be holding an album, open it and watch the case open along the hinge, or fold it like a laptop and flip through a CD wallet above the deck. And it is for iPad users who want the carousel and glowing display visible on a stand, any way up, while albums play from their library. Penta is aimed at iPhone and iPad users with an Apple Music library who want to play albums the old-fashioned way, and at iPhone Duo owners curious about what a foldable screen adds to a music app. It is available on the App Store and free on iPhone and iPad, with an optional one-time Pro unlock for the real printed CD art. iPhone Duo support arrives with Penta 1.3 as a free update for everyone who has the app. Support is handled by email at jaseubert@gmail.com, and the developer says every message is read. Penta's value is simple: it makes a digital library behave like a five-disc CD changer, with the case, the carousel, the glowing display and the recorded sounds of real hardware. For anyone who misses the physical ritual of playing an album, that is the whole point.