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No-Code AI Tools
Discover and compare the best no-code AI tools and software. Browse 104+ curated tools with reviews and rankings.
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WZRD is an AI workspace built for teams and creators who work with documents, slides, forms and sheets. Its central promise is that these everyday business artifacts should not be static files that people simply read, scroll through or fill in; instead, WZRD turns them into interactive, conversational experiences that talk back. The platform lets a user upload existing work or generate new material from a prompt, and then reshapes that material into an AI experience that end users can engage with directly. WZRD is described as an AI-native workspace that produces AI presentations in minutes, smart form flows, smart sheets with structured generation, and AI-crafted smart documents. In short, it is a single AI workspace where the documents a business already relies on become responsive, voice-enabled assets. Most business communication still runs on static formats. A slide deck is presented once and then sits inert as a file. A form is a passive set of fields that a respondent has to interpret and manually complete. A spreadsheet holds numbers that rarely explain themselves. A document is read, not conversed with. WZRD addresses this gap by making the output conversational and responsive rather than fixed. The problem it targets is the distance between the information a team has produced and the ability of an audience to engage with that information. By converting documents, slides, forms and sheets into AI experiences, WZRD aims to keep the audience involved instead of leaving them as passive readers or viewers. That shift matters for teams and creators who need their work — pitches, reports, intake forms, analyses — to be understood and used, not just delivered. On the presentation side, WZRD generates AI presentations in minutes and brands them as slides that talk back. The idea is to give slides a voice that walks people through the story, so instead of a silent deck the audience is guided through the narrative. Users can drop a file to start or browse their documents, and WZRD will work from existing material rather than requiring everything to be built from scratch. For documents, WZRD produces AI-crafted smart documents — written artifacts that, like the rest of the output, remain conversational and engage every user rather than being one-directional text. Both formats are generated with AI and can originate either from an uploaded file or from a prompt, which means the same workspace can be used to refresh existing content or to create something entirely new. WZRD also covers the data-collection and data-explanation sides of business work. Its forms are described as smart form flows that collect answers by speaking or typing, so a respondent is not limited to typing into fields — they can answer out loud, and the output stays conversational and responsive. Sheets, meanwhile, are smart sheets with structured generation, and they explain numbers out loud. Rather than leaving a reader to interpret a grid of figures, a WZRD sheet can narrate what the numbers mean. These two capabilities extend the same underlying principle — that business artifacts should be able to communicate — into the workflows of gathering information and understanding quantitative results. Voice is a first-class part of the product. The site invites users to tap to talk, and the product is positioned around talking to your business with AI voice agents. That means the experiences WZRD produces are not limited to text on a page: a person can speak to the material and receive a spoken or conversational response. Combined with the description that the output stays conversational and responsive, this points to an approach where the finished artifact behaves more like an interface than a file. Whether the artifact is a form gathering spoken answers, a sheet explaining figures out loud, or slides walking someone through a story, the common thread is a two-way interaction rather than a one-way read. WZRD's overall method is deliberately simple to start. A user begins by answering the question of what they are building today, then either drops a file or clicks to browse their documents. The supported starting formats listed on the site are XLS, CSV, PPT, PDF and DOCS, which means spreadsheets, presentations, PDFs and word-processor documents can all serve as the raw material. From there, the user can generate with AI, producing slides, forms, sheets or docs as needed. Alternatively, material can be created from a prompt without an existing file. The result is then turned into an AI experience that users can engage with — an experience that stays conversational and responsive. This workflow keeps the entry point familiar (upload what you already have) while changing the destination (an interactive AI artifact instead of a static file). The benefit WZRD claims for its users is a more engaged audience and less friction in producing polished business material. AI presentations arrive in minutes rather than requiring hours of manual formatting. Forms gather answers by voice or by typing, which can make responding easier. Sheets articulate their own numbers. Documents engage every user instead of sitting unread. For teams and creators, that combination means the work they publish continues to communicate after it is sent, and the people on the other end have a way to interact with it. The output remaining conversational and responsive is the through-line: the same material that used to be inert now responds. Concrete scenarios follow directly from the four formats. A team preparing a pitch or internal briefing can turn an existing PPT or PDF into slides that talk back, walking the audience through the story rather than leaving them to read the deck alone. A business collecting information can build smart form flows that accept spoken or typed answers, which suits intake, feedback or discovery processes. An analyst or operator can use smart sheets with structured generation so that a spreadsheet explains its numbers out loud instead of requiring the reader to decode them. A writer or team lead can produce AI-crafted smart documents that engage readers conversationally. And because users can upload existing work, all of these scenarios can start from material a team has already created and then be reshaped into an AI experience. WZRD states its audience plainly: it is for teams and creators working with documents, slides, forms and sheets. The site does not detail specific integrations, a public technology stack, or pricing plans, so those remain unspecified here. What is stated about getting started is the supported input formats — XLS, CSV, PPT, PDF and DOCS — along with two creation paths, uploading a file or generating from a prompt. The product was also launched on Product Hunt, which is referenced on the site as a live launch page. WZRD's core value proposition is straightforward: an AI workspace where the documents, slides, forms and sheets a team already works with are regenerated as AI experiences that talk back. By combining generation from a prompt or an uploaded file with voice-enabled, conversational output across four common business formats, WZRD aims to move everyday material from static delivery to responsive engagement.
gr.Workflow is a visual, node-based AI pipeline builder built into Gradio. It lets you chain together Hugging Face Spaces, models, datasets, and your own Python functions on a drag-and-drop canvas. The simplest possible Workflow app is a single line of code: gr.Workflow().launch(). You then open the app, drag Spaces, models, and datasets from the sidebar onto the canvas, connect their ports, and hit Run. The guide describes gr.Workflow as already being a complete Gradio app that must be created at the top level and cannot be nested inside a gr.Blocks context. It is designed for people who want to assemble multi-step AI pipelines visually while keeping their own Python code available as callable nodes on the same canvas. Building an AI pipeline has traditionally meant writing glue code to move data between models and services, downloading weights, and rebuilding the entire pipeline whenever a better model appears. Gradio Workflow is presented as a way around that: pipelines are assembled from nodes on a visual canvas, intermediate inputs and outputs can be inspected, and better models can be swapped in without rebuilding the workflow. Because the nodes call hosted Spaces, models, and datasets, nothing has to be downloaded. The finished pipeline is not locked inside a private session either. It can be shared with a URL or run via a REST API, and its topology is stored in a portable workflow.json file that a coding agent can write or edit, which means workflows can also be created programmatically. On the canvas, a workflow is organized into three node collections, which the guide defines precisely. References are the inputs: uploaded files, editable text, and literal values. Operators are the processing steps: Spaces, models, datasets, and Python functions. Subjects are the outputs, the results being created. As you add, remove, or change nodes and edges, a workflow.json file is automatically created next to the Python script that created the Workflow; you can pass graph= if you want to save it somewhere else. That autosave behavior means the canvas and the file stay in sync without manual export. Node geometry is optional too: include x and y on every node to control how the graph is arranged the first time someone opens it, or leave them out and the canvas auto-arranges the layout instead. Your own Python code becomes part of the pipeline through bind=. Functions passed via bind= appear as callable nodes on the canvas, and Gradio inspects the function signature to auto-generate input and output ports. The guide gives the example of a summarize function that takes a string and returns a string; using a dictionary, for example bind={"My Summarizer": summarize}, gives the node an explicit name. Signature inference is intentionally simple. Parameters annotated as int or float become number ports, bool becomes boolean, and strings, unannotated parameters, and other annotations default to text. Gradio initially generates one output port for each bound function, and any media ports or multiple outputs must be defined explicitly in the workflow JSON. Bound functions can also be wired together in code with edges, a list of (from_fn, to_fn) tuples referring to functions in bind=, using fn_name.port_label to target a specific port when a node has multiple inputs or outputs. The guide notes that edges= only connects bound Python functions while generating a new workflow: it cannot create edges to Space, model, or dataset nodes, it is ignored when the workflow file already exists, and the file must be deleted to regenerate the initial topology. Workflows can also be loaded from disk. Passing a graph= path such as graph="workflow.json" loads a saved workflow topology; the canvas reads from that file on each page load and autosaves back to it whenever nodes or edges change, and if the file does not exist yet it is created on the first authorized edit. The guide is explicit that bind= does not automatically add or wire functions into an existing graph: to combine an existing graph with bound functions you either add the functions from the canvas Functions menu, or include an operator with "kind": "fn" whose "fn" value exactly matches a key passed through bind=. Layout remains flexible for viewers: the file's arrangement is only a starting point, each visitor is free to drag and resize cards, that arrangement is saved in the visitor's own browser rather than in the file, and workflow.json is never rewritten with it. Height is measured from the rendered card, and width is the default every viewer starts from, with a writer's resize becoming the new default the next time an edit is saved. Under the hood, the workflow JSON defines a schema_version, a name, the three node collections, and a list of edges that connect node IDs and port IDs with a stated type. Operators come in four kinds. A space node calls a Gradio Space on the Hub via gradio_client, configured with space_id and endpoint. A model node calls a Hugging Face model via InferenceClient, configured with model_id and a supported endpoint such as text_to_image, while pipeline_tag is also stored for discovery and compatibility with older graphs. A dataset node pulls one row from a Hub dataset per run, selected by the row_index input, configured with dataset_id, dataset_config, and dataset_split. An fn node calls a Python function whose fn value matches a key passed via bind=. Ports are typed so the canvas can validate connections, with support for image, audio, video, text, number, boolean, gallery, file, json, model3d, and any; any is a compatibility fallback that can connect to every port type, and file and any usually come from API schema inference rather than being offered as reference or subject templates in the canvas picker. Pipelines can also fan out: one reference can feed multiple operators simultaneously, and when you run the workflow in the interactive canvas, operators at the same dependency depth run in parallel. The guide adds that when the same workflow is invoked through its generated Gradio API, the server currently executes those branches sequentially. The benefits described in the guide follow directly from this design. Because nodes are wired on a canvas rather than buried in code, intermediate inputs and outputs can be inspected while a pipeline runs, which makes multi-step AI systems easier to debug and reason about. Because each operator is a separate node, a better model can be swapped in without rebuilding the workflow. Nothing needs to be downloaded, since the operators call hosted Spaces, models, and datasets. Shipping is flexible: share links and the ordinary local URL are run-only, while launch() also prints a private write-access URL for editing and saving the workflow, which the guide advises keeping private because edits affect the workflow seen by every visitor. Finally, the workflow.json file is both an autosave artifact and a programmable surface, so the same topology can be authored by hand, by a coding agent, or by bind= and edges= declarations in Python. The guide's fan-out example is a concrete, described workflow: a single product photo reference is fed into four FLUX Kontext branches, showing how one input can drive multiple image transforms at once. Text pipelines are demonstrated in the edges example, where a clean function that strips and lowercases text is wired into a tag function that prefixes the processed text. Model nodes such as black-forest-labs/FLUX.1-schnell with the text_to_image endpoint illustrate text-to-image generation inside a workflow, while dataset nodes make it possible to run a pipeline over a specific row of a Hub dataset, controlled by row_index. Because the same app can be shared with a URL or invoked through a generated Gradio API, the guide frames Workflows both as an interactive canvas for building and inspecting a pipeline and as a deployable app that others can run without editing it. The product is aimed at people building AI pipelines in Python: the guide's code samples are Python throughout, and the canvas is an extension of the Gradio app that the function nodes live in. Integrations called out in the content are the Hugging Face ecosystem, including Spaces on the Hub through gradio_client, models through InferenceClient, and Hub datasets, together with your own Python functions and the generated Gradio API. The relevant technology stack therefore includes Python, Gradio itself, gradio_client, InferenceClient, and REST access to the running app. The guide positions gr.Workflow as part of Gradio's Additional Features, sitting alongside the library's other tutorials and guides, and it must be created at the top level as its own app. No pricing or plan details are stated in the material reviewed here. In short, gr.Workflow brings a visual, node-based editing experience to AI pipelines without taking developers out of Python. You drag Spaces, models, datasets, and your own functions onto a canvas, connect typed ports, and run the result as a Gradio app that can be shared with a URL or called through a REST API. The topology is portable in workflow.json, editable by hand or by a coding agent, and free of downloads because the heavy lifting happens on hosted Hugging Face resources. The primary value proposition is straightforward: build, inspect, and iterate on multi-step AI pipelines visually, and swap in better models without rebuilding the workflow.
Minicart is a platform that lets makers, creators and resellers launch and run an online store by chatting with AI. Its central promise is simple: take a photo of what you make, answer a few quick questions, and Minicart builds a real website that you own, complete with a polished product listing, a pricing suggestion and a full storefront. From there, a team of AI teammates runs the store day to day. The product is aimed at people who want to sell online without learning ecommerce software — the site puts it as "if you can text, you can run a store." Everything is managed through a simple chat interface, with no code, no design work and nothing to configure, and the site stresses that nothing goes out without your say-so. Traditional ecommerce platforms ask a lot of a first-time seller. Building a store means choosing themes, writing listings, configuring payments, learning dashboards and app stacks, and often paying monthly fees plus listing fees on top. Marketplace sellers face a different problem: their storefront is a booth inside someone else's feed, so the brand, the domain and the customer relationship all belong to the platform. Minicart frames its purpose around both frustrations. The site describes its store as "a store that's actually yours" — your brand, your domain, your customers — rather than "a booth in someone else's marketplace." It also states that there are no listing fees, ever, and that Minicart only charges a small card fee when you actually make a sale, and that it only earns when you earn. The first capability is photo-to-store creation. You snap a picture of what you make and answer a few quick questions, and Minicart turns that photo into a polished product listing — title, description and pricing suggestion — and a full website, automatically. The site says one photo is enough to get started and that a store can be live in under 10 minutes. In the example shown on the page, the assistant takes a single candle photo and reports that it built four product photos from it and launched the store. The workflow is described as three steps: snap a photo, we build your store, then go live and sell. Your own domain and brand come with it, and the page repeats that there are no listing fees, ever. Second, Minicart handles sellers who are already selling elsewhere with one-click imports. You paste your Etsy shop link, your Shopify store link, or your eBay store or seller link, and Minicart pulls in your listings — titles, prices, photos and variations — and rebuilds them on a site you own. The site says a full catalog is imported in minutes, with no exporting, no copy-paste and no re-uploading photos. Crucially, the connection is described as read-only: Minicart states it never touches or pauses your existing store, so you can keep selling on the marketplace while you build. For Etsy users it highlights keeping the shop open and running while finally owning the customer list; for Shopify users it highlights being live in 10 minutes rather than weeks, dropping the monthly fees and app stack, and letting three AI teammates run the busywork; for eBay users it highlights importing listings in minutes while continuing to sell on eBay and owning the customer, not just the sale. Third, and at the heart of the product, are the three AI teammates who work 24/7. Sloane, the storefront teammate, builds your site, takes payments, tracks your sales and keeps your listings current. Milo, the marketing teammate, drafts lifestyle photos, social posts and discount codes that bring people in. Logan, the logistics teammate, ships orders, tracks inventory, handles refunds and drafts your customer replies. The page shows a day in the life: Sloane turning your photos into three products, Milo drafting Instagram posts for those three new products, Logan prepping a shipping label for an order and writing your reply to a customer question, and Sloane processing the day's payments and sending your payout. Each item is marked done or ready, reinforcing that the owner remains in control of everything the team produces. Fourth, everything is operated through chat, including bulk changes. The site describes the interface as "if you can text, you can run a store": you manage inventory, draft social posts and talk to customers by sending plain-language messages. A dedicated bulk editor lets you update everything at once — for example, "increase price of all red necklaces by $5" — and your team lines up the changes, figures out what is affected and shows you a list to review. Nothing changes until you confirm: you can check or uncheck anything you want to skip and then approve. The same pattern applies to inventory updates, such as reporting that you have made five new blue butterfly bracelets and 12 ruby necklaces and confirming the new stock numbers. The site states plainly that you are always in control and nothing goes out without your say-so. Fifth, Minicart emphasizes ownership and economics. The store lives on your own domain under your brand — the FAQ notes it runs on your own Minicart domain, for example yourstore.minicart.com, and that you can connect a custom domain you own. You can bring your own domain or grab a new one, and the store carries your name, colors and style from day one. Your customer list is yours to keep and grow rather than locked inside a marketplace, which the site ties to building repeat customers and a brand you can grow for years. There are no listing fees — list as much as you want — and Minicart says it only charges a small card fee when you actually make a sale. Taken together, the approach is to replace ecommerce software with a chat-driven AI team. Instead of learning a dashboard, you describe what you need in plain language, and the AI teammates figure out what is affected, do the work and present it for your review. Launch can start from a single photo or from an imported catalog, and operations continue as a running dialogue: turn photos into products, draft an Instagram post, create a promo code, prep a shipping label, write a customer reply, process payments and send a payout. The site summarizes this as "see it in action — watch how your simple text commands trigger a complex chain of storefront updates." The stated control model is consistent throughout: the team proposes, you approve. The promised outcomes are speed, control and ownership. Speed: a store live in 10 minutes, a full catalog imported in minutes, and 24/7 AI teammates rather than hiring help. Control: every action is reviewed and confirmed before it goes out, with a checklist you can edit before approving. Ownership: your own website, your own domain, your own brand and your customer emails. The site also stresses economics — no listing fees, ever, and paid plans that lower card rates. Real stores built with Minicart are showcased on the page: Butterfly Boutique, Fame City Card Co, Trammell's What The Flip, Treasured Tees and Things and Diecast Car Culture, described as selling trading cards, sneakers, boutique gifts and more, with owners who launched fast and run their business by chatting with their AI teammates. Concrete scenarios appear throughout the page. A maker photographs what they create and Minicart turns it into a listing and a live store, with one example showing a candle photo producing four product photos. A bracelet maker tells the team to create a 20% summer promo code for all bracelets and gets confirmation that all bracelets are now 20% off with code SUMMER20. A seller asks for an Instagram post for a blue butterfly bracelet. An owner says to increase the price of all red necklaces by $5 and reviews five affected items with their old and new prices. A seller reports new inventory — five blue butterfly bracelets and 12 ruby necklaces — and confirms the updated numbers. And existing marketplace sellers paste an Etsy, Shopify or eBay link and get their catalog rebuilt on a site they own. Minicart is built for makers, creators and resellers — including craft sellers, trading card and sneaker resellers, boutique gift shops and apparel sellers — who want to sell online without learning ecommerce software or managing a booth inside a marketplace. The FAQ emphasizes that no tech or design skills are required, that there is nothing to code, design or configure, and that the AI teammates handle the setup and the day-to-day. On pricing, the site states there is a generous free plan where there are no monthly fees to start selling, that paid plans lower Minicart's card rates, and that no credit card is required to start. A current promotion offers 45% off the Assistant plan plus a free custom domain. Minicart's core value proposition is to let anyone turn what they make into a store they own and then hand the busywork to AI. A photo or an imported catalog becomes a live website in minutes; Sloane, Milo and Logan keep it running around the clock; and every change is confirmed by you before it ships. For sellers tired of marketplace dependence and complicated ecommerce software, Minicart offers a simpler path: take a photo, review your team's work, and keep building your business.
Bolt Forge is an agent inside Bolt.new, the AI app builder, that runs on open-source AI models only. It launches on September 14, 2026 as a research preview and appears as a third agent in the agent picker alongside the Standard and Max agents that Bolt users already work with. Every individual Pro plan receives up to 50X more usage at no extra cost through October 14, 2026, which lets builders draft, test, tear down and rebuild without rationing prompts. Forge is designed for people who arrive at Bolt with an idea and the skill to see it through, and who want room to experiment widely before spending premium credits on polished production work. Price has decided who gets to build with AI at speed and scale. Builders arrive at Bolt with an idea and the skill to see it through, then ration prompts like fuel: every brainstorm, every rough draft, every dead end burns credits priced for polished work. The drafting phase of building, the part where you need room to wander, is exactly the part usage caps punish. Forge removes that penalty. The allocation is big enough to draft, test, tear down and rebuild, and it is where builders can test a big idea, play around with new concepts and experiment as far outside the box as they want to go. A second motivation sits on the model side. AI inference costs have dropped 280x in 18 months (Stanford HAI AI Index, 2025), and that collapse is what makes an allocation this size possible. Open-source models also improve when they see how real software gets built, the one thing that cannot be scraped. Opted-in Forge sessions provide it, with consent. Builders get room, and open models get better. Forge runs open models that builders can see and select. As of September 2026 that means GLM 5.3 Flash and GLM 5.3 alongside it, with Kimi K3 and DeepSeek v4 Pro as experimental options. The lineup will evolve, and when a new open model drops, Forge is the first place in Bolt it lands. Bolt documents two honest caveats. These models are experimental inside Bolt, so the guidance is to duplicate a project before switching a serious build into Forge and to keep complex production work in Standard or Max. The models also do not all cost the same to run: Kimi K3 and DeepSeek v4 Pro burn through usage faster than the GLM pair, so starting on the default and reaching for the heavier options when the work calls for it is the recommended path. Forge also cannot take PDF uploads yet. Capability was tested before shipping. The Forge lineup ran through the Bolt Build Index, Bolt.new's own benchmark for how well a model completes real Bolt projects, and came out at 91% of the top paid model's score (Claude Opus 5) as of September 2026. In raw numbers, Forge's open models score 92.2 against 101.0 for the top paid model in Bolt.new. The trade-off is nine points; the payoff is up to 50X more usage at no extra cost on every individual Pro plan. Bolt runs Forge on its own reserved hardware instead of paying a provider per request. A fixed, predictable cost means more of what a builder pays goes to building instead of markup. Forge projects run in the browser on WebContainers, the technology StackBlitz built and Bolt runs on, so no server is rented for every build. Builds stay fast and costs stay low. Between the reserved hardware and the browser runtime, the cost structure is the reason Forge's price works. Every individual Pro plan includes the Forge allocation at no extra cost through October 14: up to 50X more usage, drawn from a single monthly bar that resets on the renewal date. Every Pro tier gets the same allowance, Forge usage is separate from Standard and Max usage, and there are no daily limits. When the bar hits 100%, Bolt switches the builder back to Standard rather than charging an overage, so there is no daily math and no surprise pause mid-project. Training in Forge is opt-in: a one-tap consent screen spells out the trade in plain language before anyone builds in Forge. The shared data is prompts, code, project files and configuration, the tool calls Bolt makes, and edit histories including the fix traces Bolt creates. Bolt de-identifies shared sessions before anything leaves its infrastructure, stripping secrets, sensitive data and personal information as a rule, and validating the pipeline against seeded test data. The sessions go into datasets that Bolt licenses to AI developers under a data license agreement. Arcee AI, a U.S. open-model lab behind the Apache 2.0-licensed Trinity model family, is the first. Bolt has partnered with Arcee to help train a trillion-parameter-class model, and sessions builders share during the preview window, September 14 to October 14, 2026, feed the first training run, which begins in October. A data license agreement governs every transfer, StackBlitz may be paid for the datasets it licenses, and those datasets may go to other AI developers as well as Arcee. What makes the corpus valuable is what is in it: the models learn from the work people do in Forge and only there, starting projects, making edits, connecting databases and fixing errors. A model that has seen the job gets it right in fewer tries. The workflow is five steps from start to finish. First, pick Forge in the agent picker, where it appears as a third agent next to Standard and Max on every individual Pro plan. Second, opt in with one tap after a consent screen that spells out the trade in plain language. Third, build, drawing Forge usage from one monthly bar with no daily limits and a hard stop at 100%. Fourth, Bolt de-identifies shared sessions before anything leaves its infrastructure. Fifth, the sessions go into datasets that Bolt licenses to AI developers, starting with Arcee AI. Switching back to Standard or Max stops Forge from collecting anything new, and builders who want Bolt to stop using Forge content it has already collected can email privacy@stackblitz.com. The benefits are framed as what the trade buys. Brainstorms, MVPs and experiments stop competing with production work for premium credits. Builders get 91% of the top paid model's Bolt Build Index score, included with Pro at no extra cost. Usage is readable at a glance: one monthly bar, no daily limits, and a hard stop instead of an overage bill. And builders get a hand in what comes next, because their sessions teach open models how real software is actually built. In practice that means scenarios such as drafting and tearing down an MVP, exploring new concepts outside the box, running throwaway experiments while premium agents stay reserved for production work, and contributing consented build sessions that feed the next generation of open models. Forge is aimed at individual Pro builders in Bolt.new. Teams and Enterprise workspaces are excluded from Forge, and from AI training and dataset licensing. Sessions from the EEA, the UK and Switzerland are not used for training or datasets, so builders in those regions get the Forge allocation without the trade. Pro is $25 a month billed yearly and includes up to 50X Forge usage at no extra cost with no access code required. Builders not on Pro can join the Bolt Lite waitlist, with codes going out in waves and the first seats opening by September 21, 2026; Bolt Lite costs $9 a month and anyone on it when sign-ups close on October 14, 2026 keeps the plan at that price. Forge is a third agent beside Standard and Max, so premium agents, allocations and privacy settings stay exactly as they are, and builders can switch between agents at any time. After October 14, 2026 the research preview window closes, but Forge keeps going as an open-model lab: the up-to-50X allocation on Pro and the first training-data window with Arcee AI both run from September 14 to October 14, and what comes next depends on what the preview shows. Forge is experimental inside Bolt, so serious production work belongs in Standard or Max. The takeaway is the trade at the centre of the product: builders trade de-identified, opted-in build sessions for up to 50X more usage on open-source models that score 91% of Bolt's top paid model. Builders get room, and open models get better.
Playly.ai is a no-code gamified marketing platform that empowers businesses to create interactive experiences that capture attention, increase customer engagement, and drive measurable results. Built for modern marketers, agencies, brands, and businesses of all sizes, Playly.ai transforms traditional marketing campaigns into engaging experiences that audiences genuinely enjoy participating in. Today's customers expect more than static advertisements and conventional lead forms. They want meaningful interactions that are personalized, memorable, and rewarding. Playly.ai helps businesses meet these expectations by enabling them to build interactive campaigns using ready-to-use templates such as quizzes, Spin & Win, scratch cards, polls, memory games, puzzles, prediction games, personality tests, and more—all without writing a single line of code.Whether you're generating leads, launching a product, promoting an event, growing your email list, collecting zero-party data, or increasing customer participation, Playly.ai provides the tools to create campaigns that turn passive visitors into active participants. Every interaction becomes an opportunity to engage audiences, gather valuable customer insights, and improve marketing performance.Designed with simplicity and flexibility in mind, Playly.ai allows marketers to customize games with their own branding, messaging, rewards, and campaign goals. Interactive experiences can be embedded on websites, landing pages, email campaigns, social media, QR codes, digital ads, and event activations, making it easy to engage customers across multiple touchpoints.Our mission is to make marketing more interactive, engaging, and enjoyable. We believe the future of marketing isn't about interrupting audiences with more advertisements it's about creating experiences that invite participation and build stronger customer relationships.Through our blog, Playly.ai shares practical insights, industry trends, campaign ideas, gamification strategies, customer engagement techniques, and interactive marketing best practices to help businesses stay ahead in an increasingly competitive digital landscape. From retail and real estate to healthcare, education, banking, hospitality, SaaS, and events, we explore how gamification can be applied across industries to improve engagement, generate quality leads, and increase conversions.At Playly.ai, we're helping brands rethink the way they connect with their audiences one interactive experience at a time.Marketing That People Want to Play.
OzBrain is a hosted knowledge base that acts as a shared brain every AI agent you use can read and write. Rather than letting each platform build its own separate, partial memory, OzBrain gives Claude, ChatGPT, Claude Code, Cursor, Gemini Spark and other connected clients one structured source of truth that agents and teammates both read from and write to. It is a place to hold your projects, decisions, research and the thinking you have already done, so a new chat can begin with what the task actually needs. It is aimed at founders, small teams and individuals whose work increasingly happens inside AI agents, and who want one brain instead of a scattered pile of chats, documents and stale file copies. The problem OzBrain addresses is fragmentation. Today the context of your work lives in your chats, your teammate's context lives in theirs, and the two never meet, so you share a doc, drop a message, and paste the same things over and over again. Meanwhile copies of the same plan sit in Drive, on laptops, in Downloads and in email, and no one is sure which one is current. Platform memory does not solve this: it keeps scraps and summaries, a few preferences and a thin summary of past chats, not the work itself. OzBrain is positioned as the layer underneath the tools you already use, holding the projects, decisions, research and prior thinking that platform memory cannot hold. The first core capability is structure. OzBrain breaks your knowledge into nested pieces, so an agent pulls the exact slice it needs rather than loading everything. The site illustrates this with a launch plan that nests into launch campaigns, which in turn nests into a single launch email: the agent can retrieve the 218-token, 0.87 KB email instead of the 11,842-token, 47.3 KB plan. Because the agent has less to load, it answers faster and more cheaply, and it is less likely to invent an answer out of context it never needed. In practice this means an agent can go straight to the email body it has to write without dragging the whole launch plan, the campaigns and the surrounding research into the conversation. The second capability is freshness. When newer thinking lands, OzBrain goes back through your knowledge on its own, marks the old material as replaced, and points to the latest version. You never have to hunt down every place an old decision lived, and no agent answers from a version you have already moved past. The site shows a company-details article being updated with a new domain, and the website-launch-plan article being updated to record that a new website URL was set and that the old domain is forwarded. Alongside this, OzBrain keeps every change on the record: which agent made it, what changed, and the reasoning behind it. Changes are proposed before they land, so several agents can work at once without writing over each other, and you can always see how the brain got to where it is. A recent-changes table on the site lists the time, the agent, the article and the reason for each edit. The third capability area is privacy and portability. Your brain is encrypted at rest and sealed to your account, so nothing leaks between tenants. OzBrain states that it never trains on your data and never sells it, describing the brain as a sovereign place for your data. On top of that, you can export everything as markdown whenever you want, including after you cancel, and deleting your account removes your content. The company frames this as a deliberate position: your knowledge is yours, you should be free to use the best tools for it, and OzBrain would rather earn your stay than lock you in. OzBrain works as a brain you connect rather than a service you code against. It is explicitly not another memory API: memory APIs sell add and search endpoints to developers building apps, while OzBrain is a hosted brain behind the connector menu that Claude and ChatGPT already show you. There is nothing to install and no coding required: you add OzBrain from the connector menu in Claude or ChatGPT, sign in, and approve it. The knowledge itself is stored as structured articles with links, provenance and freshness. Every write is staged, routed and checked against what the brain already holds, which is what separates it from a notes app: notes are written by you for you, while a brain is written by your agents for your agents. The site also describes it as one URL, so anything that speaks the protocol can hold the same brain. The benefits described follow from that approach. Agents pull only what they need, so they make fewer mistakes and answer faster and more cheaply. Your new and better ideas replace old notes automatically, so you stop chasing outdated versions scattered across drives, downloads, chats and email. Because every change is recorded with its reasoning and proposed before it lands, multiple agents can work simultaneously without overwriting each other, and you can audit how the brain arrived at its current state. Your data stays encrypted and private, and you retain the ability to take it with you as plain markdown at any time. Concrete scenarios in the content include teams where every member's agents work on the same knowledge: point everyone at one OzBrain, and what one person works out is already available to everyone's agents. A launch workflow shows an agent retrieving just the launch email slice instead of the full plan. A domain migration shows an update to company details propagating so agents no longer answer from an outdated domain. A solo founder's export shows seven brains and 503 articles being exported as markdown, and an enterprise scenario shows org-owned shared brains that every seat's agents share. OzBrain connects to Claude and ChatGPT through their native connector flows, plus Claude Code, Cursor, OpenClaw, Hermes Agent and Gemini Spark where Google makes it available (US, Spark eligibility), and any client that supports connectors. Pricing starts free: the Free plan is $0 forever with up to 50 articles, sharing on brains you own, unlimited brains, reads, writes and connections, write-time size discipline, and markdown export anytime. Pro is $20 per month with up to 500 articles for one venture plus personal knowledge, and Max is $99 per month with up to 5,000 articles for when agents run the operation from one brain. Both paid tiers include unlimited brains, reads, writes and connections plus markdown export anytime. Enterprise adds org-owned shared brains above the Max ceiling with per-seat pricing designed with you. The site also publishes comparisons against Mem0 and Supermemory, ChatGPT Memory, Claude Projects, ChatGPT Projects, Notion and Obsidian. The takeaway is straightforward: OzBrain is the shared, structured, portable knowledge layer beneath every AI agent you use, so your agents and your teammates stop working from separate partial memories and start working from one current, auditable source of truth that you can export and leave with whenever you choose.
GLYPH Immersive is a free, browser-based tool for modular grid lettering. It lets you draw rounded-pixel letterforms and shapes on an adjustable grid, with dedicated controls for spacing, corners, and edges, and it runs in the browser with no signup required. The board supports grid-based drawing more broadly: you paint cells to build letterforms and shapes, insert a photo and save it as a soft reference overlay or as editable dot-grid art, and download the result in several formats. The site describes it as "a simple space that balances possibility with constraint, encouraging experimentation, play, and reflection about the systems that we inherit - no matter how basic they may seem." The tool's own guide places it in a long lineage of grid thinking. Long before the digital pixel, the site notes, humans used math to create precise guides for art, architecture, and the page; weaving, tiling, and writing were the foundation for humankind's earliest grids. Andean weavers encoded modular symbols across bands of textiles, Ibn Muqla proportioned Arabic script using rhombic dots, kolam artists built patterns across fields of points, and Hangul assembled circles, squares, and lines into 1:1 blocks. Later, the Bauhaus stripped design to those same elementary parts, with Anni Albers crediting Andean weavers as her teachers. The early digital age relied on the same foundation, using pixels to create efficient symbols on tiny screens, and that era inspired novel approaches to the grid such as Wim Crouwel's New Alphabet, Gego's Reticulareas, Lance Wyman's Huichol-inspired Mexico 68, and Cordeiro's computer art. Cordeiro, the site explains, was optimistic about electronic art, or arteonica, believing that computation belonged to artists. GLYPH Immersive sits in that tradition: a simple space that balances possibility with constraint, at a time when technology has continued to present new possibilities while at times numbing life's necessary friction - the constraints and discomfort that produce great art and ideas. Drawing happens directly on the board. You click or drag to paint, and Shift+click fills a line between two cells. Clicking through a cell cycles its joints: the first click fills and joins a cell, the second click removes joints, and the third click resets it. The board can be rendered as Grid, Dots, or Vertex, which changes how the underlying system of cells, points, and connections is displayed while you work. Corners and Edges settings shape how the letterforms and shapes resolve, and a Detail/Invert option is available for reviewing the artwork. A Preview Type control and a Clear board action sit alongside these tools, so a design can be checked and reset without leaving the page. Two menus organize the work. The control menu adjusts the artwork itself, while the guide menu controls the artboard. Width, Height, and Spacing controls determine the size of the grid and how much room sits between forms, and guides can be added to the board. The Type (beta) panel inside the guide menu exposes Font name, Columns with Gutter, and Rows with Gutter settings. Color, accessed in the guide menu, cycles fill and background modes. Wander plays a calm loop of designs, useful for watching a system unfold rather than drawing it by hand. Navigation is done by scrolling or two-finger dragging to pan, and by pinching or using the Zoom slider to zoom; the zoom level is shown as a percentage. The guide menu also presents the grid, dot, and vertex views along with Sound and a Language option shown as EN and ES. Reference imagery is a first-class part of the workflow. You insert a photo, place it on the board, and then choose to Save as reference, which lays it down as a soft overlay beneath your drawing, or Save as dots, which turns it into an editable grid artwork made of cells. A Cancel option lets you back out before committing. Once a design is finished, Download offers SVG, PNG, JPG, ASCII MAP, and TYPE formats, with TYPE available when Create type is switched on. That combination means the output can be a vector file, a raster image, a text-based map, or an installable font, depending on what the project needs. GLYPH includes a Type tool for building a font from scratch. The site calls it "a simple way to create your own font." When Create type is on, each labeled green box on the board represents one character, covering A-Z, digits, and punctuation. You adjust the guides to change width, height, and spacing, then paint inside the boxes to design each letter. When the glyphs are ready, downloading TYPE exports a basic OTF file that you can install and try out in other software. The overall approach is deliberately minimal. One menu controls the artwork, the other controls the artboard, so the settings that define the system stay separate from the marks you make inside it. Grid, Dots, and Vertex views let you look at the same construction through different lenses, while Color and Sound settings sit in the guide menu together with the grid and guide controls. Free export, no signup, and browser-only operation keep the barrier to entry at zero, and GLYPH Immersive is published as an open source resource by Kate Ander. Because it is free, requires no signup, and runs in the browser, GLYPH Immersive removes the usual setup friction from grid drawing and type design - there is no account, no install, and no export paywall. The constraint of the grid, paired with adjustable spacing, corners, and edges, keeps outcomes coherent while leaving room to experiment, which is exactly the balance the project sets out to hold. The export options mean the work is not trapped in the tool: vector and raster files can move into other design software, ASCII MAP offers a text-based representation, and TYPE produces a font that can be installed and tried. Concrete uses described on the site include drawing rounded-pixel letterforms and shapes on the grid, designing a full set of characters in the Type tool and exporting an OTF font to install and try, turning a photograph into editable dot grid art with Save as dots, and laying a photo down as a soft reference overlay to paint against. Wander mode supports a different, more passive use by letting a calm loop of designs play. The Clear board action, the guides, and the grid, dot, and vertex views make it practical to iterate through many variations of a single system. GLYPH Immersive is aimed at anyone working with letterforms, grids, and modular systems - designers and typographers building type and grid art, artists experimenting with pixel and dot constructions, and educators or students exploring grid systems and their history. The tool is web-based and free, with no signup required, and it is released as an open source resource. The site also offers occasional email updates about GLYPH and new work from Kate Ander, and questions, ideas, and bugs can be sent to glyph@kateander.com. GLYPH Immersive is a free, open source, browser-based space for modular grid lettering and grid art. It pairs a simple adjustable grid with corners, edges, spacing, reference-photo tools, and free export to SVG, PNG, JPG, ASCII MAP, or OTF - inviting experimentation and play with the systems we inherit.
Viso Now is a self-building AI vision platform that turns images, video, and camera feeds into working computer vision applications. Rather than assembling a machine learning pipeline from scratch, users describe in plain language what they want to understand in their footage, and Viso Now builds the agentic vision logic and the custom live dashboards to go with it. The website positions the product around a single promise: bring a new AI vision application to life. It is designed for anyone who can describe a problem, from individuals who want a vision agent running in minutes to enterprises that need governed vision intelligence across many sites and cameras. The problem Viso Now addresses is the cost and complexity that traditionally surrounds computer vision. Building a vision application normally means model training, image annotation, ML engineering and ongoing model maintenance, which puts bespoke vision projects out of reach for most operational teams. Viso's own messaging states that isolated solutions are no longer enough and that not all computer vision is equal: single-purpose tools lease one outcome for one use case, while Viso positions its platform around multiple use cases across multiple locations. The site argues for flexible solutions with a lower total cost of ownership, complete control of your data, and the ability to customise outcomes and hit KPIs, and for handling today's challenges while preparing for tomorrow's opportunities. The core of Viso Now is prompt-driven building. A user describes the real-world situation they want AI to solve in plain language, with no model training and no annotation needed, and watches as Viso builds the application with them in real time. The site describes this as starting from a prompt, testing instantly, and turning ideas into vision agents. An 'Ask Viso to create a camera agent' flow sits directly in the product interface, where users work alongside the builder and refine the application until they are happy with the finished solution. Viso describes the underlying capability as visual general intelligence that applies to any use case, which is why the same engine can be pointed at very different operational questions without rebuilding anything from the ground up. Supporting that building process is a template gallery. Viso Now shows ready-made templates including Task-Aware PPE Check, Excavator Near-Miss Monitor, Hot Work Safety Check, Work-at-Height Safety Check, Clinical PPE Protocol Check, GMP Hygiene Check, Loading Dock Exclusion Zone, Front Desk Wait Tracking, Restricted Site Vehicle Alert, Hazardous Area PPE Check, Pipeline Integrity Scout, Visible Release Detection, Robot Cell Intrusion Detection, Production Area Access Check, 5S Shop Floor Audit, Emergency Exit Clearance, Reversing Vehicle Danger Zone and MEWP Fall Protection Check. Further templates cover Dock Turnaround Intelligence, Check-in Queue Orchestrator, HSE Workplace Audit, Commercial Vehicle Safety Screening, Dump Zone Safety Inspector, Abandoned Luggage Response, Handling Risk Assessment and Service Queue Pressure Analysis. Users can also feed the system their own media by clicking to upload or dragging a video in, with MP4, MOV, MKV, PNG and JPG supported, by capturing a photo, or by recording video, and they can choose between Fast, Balanced and In-Depth analysis modes depending on how much detail the task needs. Once an application behaves as intended, the workflow moves to deployment. Users iterate on the app until they are satisfied with the finished solution, then connect cameras or upload connectors to start using it immediately. Viso handles the end-to-end infrastructure behind the scenes, covering compute, visual analysis, governance, authentication and integrations, so teams do not have to stitch those layers together themselves. The platform produces custom live dashboards as part of the build, which turn what the camera agent observes into something an operator can actually monitor and act on rather than raw detections. Viso Now's distinctive approach is that the application builds itself. The website frames this as 'Your idea, the vision app builds itself' and 'If you can describe it, you can build it.' Instead of choosing from a fixed catalogue of detection types, users state the outcome they want and the platform constructs the agentic vision logic around that outcome. Build, refine, go live is the entire loop, and the security and infrastructure concerns are handled by the platform rather than by the user. The site summarises the free tier as dropping any video, describing what to build, and getting a working vision application in minutes, with no labelling, no training and no big team required. Viso reports concrete outcomes on its website. A customer story from a global manufacturer states that the company replaced four point solutions with one Viso deployment and that, by month three, near-miss incidents were down 54%, with the safety team spending zero hours rebuilding models. The same page cites 24/7 eyes on every camera that never blink and never tire, 10x faster AI vision versus other methods, and 90% less ML engineering effort with no labelling and maintenance. Elsewhere the site claims a 10x faster understanding of visual data to drive efficiency, automation and innovation, and an 85% reduction in time-to-value of computer vision applications. For teams, the practical benefit is speed: ideas become running vision agents without a dedicated ML function, and the same deployment keeps working as requirements change. The template library also shows the concrete scenarios the product is used for. In construction, Task-Aware PPE Check, Excavator Near-Miss Monitor, Hot Work Safety Check, Work-at-Height Safety Check and MEWP Fall Protection Check address worker safety and site hazards. In oil, gas and energy, Pipeline Integrity Scout, Visible Release Detection, Restricted Site Vehicle Alert and Hazardous Area PPE Check cover asset inspection and restricted zones. In manufacturing, Robot Cell Intrusion Detection, Production Area Access Check and 5S Shop Floor Audit support safety, access control and lean audits. In logistics and warehousing, Loading Dock Exclusion Zone, Dock Turnaround Intelligence, Dump Zone Safety Inspector and HSE Workplace Audit assess dock performance and workplace safety. Healthcare, food and beverage templates such as Clinical PPE Protocol Check and GMP Hygiene Check cover protocol and hygiene compliance, while Front Desk Wait Tracking, Service Queue Pressure Analysis, Check-in Queue Orchestrator, Abandoned Luggage Response and Commercial Vehicle Safety Screening serve hospitality, public venues and transport environments. Two products sit on one platform. Viso Now is the free entry point, marked 'Free · No Credit Card', offering a free forever tier with the ability to invite your team, prompt-to-agent building in minutes, visual general intelligence for any use case, and seamless connection to other systems. Viso Suite is the enterprise option, described as the complete operating system for enterprise vision intelligence: connect every camera across every site, build governed applications, and operate agentic workflows at scale, with support for 10,000+ cameras and hundreds of sites, full lifecycle from build to deploy to govern to scale, edge AI with on-prem or cloud support, and compliance with SOC 2, ISO 27001, GDPR and CCPA. Viso also states it is trusted by Fortune 500 companies and lists 136+ applications tuned for every industry. Viso Now's value proposition is straightforward: describe the real-world situation you want AI to solve, and the platform builds, refines and runs the vision application for you. No model training, no annotation, no code writing, and no large team, but with the same platform able to scale into governed enterprise deployments across thousands of cameras. It turns any camera into an analyst that can detect, inspect, alert and understand what is happening in the physical world.
Diiverge is a web platform that turns a single picture into an explorable world you can play. Start with any photo, painting or screenshot and it becomes a point-and-click adventure: click anything in the frame, choose what happens to it, and AI generates the next scene together with a short film of the moment in between. Each world is called a volume, and a volume is a persistent adventure that grows as people explore it. Every path a visitor takes is saved, so anyone arriving later can walk the same route, and the volume's map shows all the branches taken so far. A picture is usually a finished thing: you look at it, and that is the end of the interaction. Diiverge treats one image as a starting point instead, asking what would happen if you could reach into the frame and act on what is there. The picture already holds a scene, objects and a mood, but there is normally no way to interact with any of it or to see a consequence. Diiverge adds that layer, namely interaction, consequence, and a record of both. Because generating a new scene costs real money, each volume of the series comes with a fixed number of free scenes and stops growing once they are used up, unless sponsors add more. That constraint shapes the experience: playing and replaying are free, while new generation is limited and shared. Interaction starts with the frame. Glowing dots mark what you can interact with, so it is clear at a glance which things in the picture are live. Behind the scenes, a segmentation model cuts the things in the frame out and makes them clickable, and a vision model decides what each thing is and what it might do. Because the choices come from a model that has looked at the image's contents, the options offered relate to the actual objects in the scene rather than a fixed script. You click something in the picture, and the choices presented to you follow from what that thing has been identified as. Once you choose what happens, Diiverge generates two outputs. An image model paints the chosen event's aftermath as the next frame, so the world moves forward visually, and a video model renders the moment between the two frames as film, so the transition itself plays as a short movie. The result is an experience that alternates between still scenes you can inspect and click, and moving moments that show how the change unfolded. A judge model reads the story so far and cuts any choice that breaks its continuity, which means every branch stays consistent with what has already happened in that world. Nothing you do is thrown away. A scene you carve becomes part of the volume for good. Every path anyone takes is saved, so each world grows as visitors explore it, and the next visitor can walk the same path you did. The volume's map shows every branch taken so far, giving a view of the story as it accumulates, and a shared link lands on the exact scene it points to. Because the scenes already exist once they have been made, replaying scenes others have already carved needs nothing at all. The studio is where you build your own adventure. It turns any photo, painting or screenshot into an explorable world of your own: upload a picture, make it a world, click anything in it, choose what happens, and watch the moment play as film. The workflow is deliberately small, since you sign in with your email, buy scenes in packs, and share the link when you are ready. Until you share it, your adventure stays private. When you do share, others can follow the paths you have made, so a personal picture can become something other people explore and extend. The public side of Diiverge is organised as a series of volumes, each a world grown from one picture. A volume starts as a single seeded image and becomes a persistent point-and-click adventure. Two volumes are shown on the site. Volume II, The Crystal Pass, is presented as the latest, opened five days earlier with 2,831 scenes so far. Volume I, The Lantern Cove, is listed in the earlier volumes section with 3,033 scenes. Because generation costs real money, each volume comes with a fixed number of free scenes and stops growing once they run out, and sponsors add more for everyone. For players, the practical benefits are straightforward. Playing is free, and replaying scenes others have already carved costs nothing at all, so exploring an existing world has no threshold. Your own choices are not lost work: a scene you carve stays in the volume permanently and can be followed by later visitors, which means the world reflects everyone who has walked through it. The map turns that accumulated activity into something visible, and shared links make it easy to point someone at a precise moment. For creators, the studio offers a way to turn a picture they already own into a shareable, explorable world, kept private until they decide otherwise. The use cases described on the site are concrete. Someone can play a public volume for free, clicking through a world such as The Crystal Pass or The Lantern Cove and branching as they go. A visitor can follow a path another player already carved, walking the same route through the volume. They can consult the volume's map to see every branch taken so far. They can open a shared link that lands on the exact scene it points to. A creator can take a photo, painting or screenshot into the studio, buy scene packs, and build their own adventure before sharing the link. And a sponsor can add scenes to a chosen volume of the series so that everyone gets more to explore. Diiverge is built for people who want to play visual, branching adventures in a browser, and for people who want to make one from a picture they already have, such as a photo, painting or screenshot, without handling the technical machinery behind scene generation. Access is through the web, with email sign-in for the studio. Playing the public volumes is free, and replaying already-carved scenes is free as well. The studio works with scene packs that are bought, and sponsorships start from $25, adding about 2 scenes per dollar to the volume chosen; sponsored scenes never expire and the sponsor's name goes on the volume's frames. In short, Diiverge's promise is that one picture is enough to begin a world. The seeded image becomes a point-and-click adventure, and clicking anything in the frame, choosing what happens, and watching AI paint the next frame and render the moment as film turns a static picture into a sequence of scenes. Because every path is saved, the world keeps growing after you leave, and the map and shared links carry that growth forward to the next visitor. Play the public volumes for free, or take a picture into the studio and make one of your own.
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Wandesk is an AI desktop environment where users can create the software applications they need simply by describing them in natural language. Its main purpose is to provide a persistent, local workspace where AI-generated applications have shape and stay, moving beyond the limitations of conversational AI interfaces for tasks that require structure and continuity. Key features include the ability to build apps through natural language description, support for plugging in various AI models (Claude Code, Codex, DeepSeek, OpenAI, Kimi, Qwen, or any OpenAI-compatible service), and shared context across all applications within the workspace. The AI maintains memory of user preferences and context that carries across every app, and all data, files, and memory are stored locally on the user's machine. The platform requires no signup or account creation and is available as a free, open-source product. The platform operates with an iterative approach to app creation: users describe their needs, the AI makes reasonable assumptions to build a first version, and then users can refine the app through chat-based corrections and adjustments. Every generated app has a clear, layered structure with UI, logic, and data components, plus an APP.md file that the AI reads before making changes to maintain scoped edits rather than complete rewrites. Applications are real, editable files that users can modify manually or have the AI adjust specific parts. Benefits include the ability to prototype useful local tools quickly without starting full development projects, complete data privacy since everything remains on the user's device, and no cloud lock-in or subscription requirements. Use cases mentioned include creating calorie trackers, reading lists, invoice generators, bill splitters, productivity trackers that reflect tasks across different categories, and fitness trackers that can share context with other applications. The product targets both developers who want to prototype throwaway tools without spinning up full projects and non-technical users who need functional applications without touching code or terminals. It's built with technologies including Tauri and Figma, runs on macOS and Windows platforms, and can integrate with MCP (Model Context Protocol) when needing to reach external services. The workspace stores data in per-app SQLite databases and memory in a shared memories table within the local application support folder.