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QApilot MCP for Android is a Model Context Protocol (MCP) server and CLI that lets developers automate tests on real Android devices and emulators from inside an AI coding client such as Claude, Cursor, or any MCP-compatible AI client. Rather than writing Appium code, users describe a test flow in plain English, and the AI agent builds a structured plan that QApilot executes step by step on the connected device. It is intended for mobile QA engineers, Android developers, and release teams who want Android test automation to run from the tools they already work in. The guide positions QApilot MCP around a simple premise: Android UI automation should not require hand-written Appium code. Test authors describe what they want to verify in ordinary language, and the system handles the mechanics of driving the device. The documentation also points out that step titles are generated automatically with a maximum of 50 characters and no XPath, so reports and the dashboard always stay readable. Before installation, the guide stresses that five prerequisites must be in place: Node.js 18+ (from nodejs.org or nvm), Java JDK 11+ (required by Appium, with JAVA_HOME set in the shell profile), the Android SDK or Studio (which installs adb and platform-tools, with ANDROID_HOME pointing to the SDK path), a USB device with USB debugging enabled or an AVD emulator verified with adb devices, and Appium 2.19.0 installed globally with the UIAutomator2 driver. Installation follows a documented sequence. The CLI is installed globally from the distribution package with a single npm command, then verified by running the server in stdio mode, where the server starts and waits for input without errors. Appium setup is described as a one-time task using pinned versions: Appium 2.19.0 and the UiAutomator2 Android driver 4.2.6, followed by starting the Appium server with the flags shown for chromedriver autodownload, adb shell access, the /wd/hub base path, and CORS, and confirming the device is visible through adb devices. The guide warns that other Appium or driver versions may break the MCP server and recommends keeping Appium running in a separate terminal before starting any test session. Connecting an AI client is done by pasting an MCP configuration block for Claude Desktop, Cursor, or OpenAI Codex, after which QApilot Mobile MCP appears in the client's connected tools list. Restarting the AI client is required after saving the configuration. Once connected, users register and log in through conversation. An account can be created by asking the AI to sign up with an email address; an activation link arrives by email and the login credentials follow. If environment credentials are set in the configuration, the client logs in automatically on first use; otherwise credentials can be supplied in a prompt. Users then create a project to hold their tests and launch their app by giving the Android package ID, with the default project and device selected automatically. From there, test steps are recorded by describing them: the AI builds a structured plan and executes each step on the device in real time. The guide shows example prompts for search flows, login flows with screenshots, navigation and assertions, scrolling and filtering, and form submission. A live preview returns a URL on every app launch call so the device screen can be watched in the browser as steps execute. After a successful run, the recorded steps can be pushed to QApilot as a saved test case for future replay, and only happy-path steps are saved; failures are excluded, and a failed step should be fixed and a passed report generated first. Saved test cases can be replayed one by one, in batches of IDs, or from an Excel sheet such as regression_suite.xlsx, with execution status checked on demand. Every session should end with a report, generated even on failure because it clears execution state so the next test can start cleanly. Reports capture step results, screenshots, errors, and timing, and are written to a dated session folder containing output.json and report.yaml, plus a scenario.feature Gherkin file when the session passes. The server also exposes a cache for XPaths and skills learned per app, with tools to fetch cache context for the current app and summarize what is cached for a given package, along with usage reporting for tokens and API consumption. The overall workflow is agent-led. The AI client lays out the test steps, and QApilot runs them: it finds the elements, waits for the screen to settle, retries when something moves, and caches what it learns so repeat runs are faster. Every passing run saves a Gherkin feature file and becomes a replayable test case, tying prompt-driven testing to a durable regression asset. The MCP server communicates over the Model Context Protocol, exposing dedicated tools for every capability, including account signup and login, project listing and selection, device listing and selection, app listing and launching, session start and stop, plan submission and execution, single manual actions such as tap, type and swipe, execution state, session status and info, preview URLs, accepting steps, replaying test cases, running Excel suites, generating reports, cache inspection, and usage tracking. For users, the benefits described are practical: no Appium code to write, test flows expressed in plain English, execution on real devices and emulators, readable auto-generated step titles, and replayable test cases that accumulate over time. Because caching is retained between runs, repeated executions are faster, and because reports include screenshots, errors and timing, failures are easier to inspect. The preview URL lets teams watch a run from the beginning, and the Excel-driven execution path supports regression batches without rewriting anything by hand. The documented use cases are drawn from everyday mobile app testing. A search flow can be tested by tapping Search, typing a search term such as Honda City, selecting the first result, and verifying the car detail page loads. A login flow can enter an email and password and screenshot the home screen. Navigation and assertions can go to a comparison screen, add two cars, and confirm the compare button is visible. A scroll-and-filter scenario can scroll the filters page, select Petrol as the fuel type, and apply the filter. Form submission scenarios can fill an enquiry form with a name, phone number and city, then submit. Saved suites can then be replayed individually, in sequence, or in bulk from an Excel regression sheet, with status checks in between. QApilot MCP for Android is aimed at mobile QA engineers, Android developers, and teams that already work inside AI coding agents such as Claude, Cursor, or OpenAI Codex, as well as any MCP-compatible client. It integrates with Appium 2.19.0 and the UiAutomator2 driver, the Android SDK and adb, and runs on Node.js 18+ with Java JDK 11+. A Slack community is available for questions, prompts, and setup help. The guide does not state pricing or plan details. In short, QApilot MCP for Android turns an AI coding agent into an Android test automation driver: describe the flow in plain English, let the agent plan it, and let QApilot execute, preview, accept, replay and report on it, with caching that makes the next run faster.
Spaces is a desktop app that gives a team one shared space per project, where the people on the team and their AI agents work side by side in the same place. Chats, files, and routines live in the space rather than in one person's private thread, so context survives from one day to the next and is visible to everyone who is in the space. It is aimed at teams that already pay for an AI model and want that model's work to end up somewhere shared instead of scattered across separate laptops. The app installs like any other desktop program, is free to start, and needs no account until you want to bring other people in. The problem Spaces is built around is described plainly on the site: five people, five private chat histories. Everyone on the team has their own AI thread and none of them can see each other's. The project's context is scattered across browser tabs on separate laptops, so every new question starts by re-explaining the job. The result, in the site's own words, is that each person gets faster while the team does not. That is the gap Spaces targets. Individual AI assistants are useful, but the reasoning, drafts, decisions, and files they produce tend to stay inside a single person's chat. By making the project — not the person — the thing that holds context, Spaces lets the standing knowledge of a project accumulate in one shared location instead of being rebuilt from scratch each time someone asks a question. The core of the product is the shared space itself: one project, one conversation. Chats and files live in the space, not in one person's thread, so a decision made on Monday is still there on Thursday for everyone. The site shows a Product Launch space as an example, managed by a Launch Lead, with a Copywriter and a Researcher also working in it. Its chat list includes "Press list for launch week" with the Launch Lead, "Waitlist copy" with the Launch Lead, and "Battery claims vs launch brief" with the Researcher, each showing when it was worked on. Alongside Chats, the space carries Files, so the documents a project depends on sit next to the conversations about them rather than in someone's downloads folder. Specialists are how Spaces replaces a single general assistant with a small team of agents. The site's instruction is to hire agents, not one assistant: a researcher, a copywriter, a launch lead. Each agent keeps its own notes, and anyone in the space can put any of them to work. Each specialist gets its own instructions, memory, and tools, which is what allows it to research, draft, work through your inbox, or run a report. In the interface, agents are added and managed from an Agents panel that distinguishes Agents, Skills, and Tools, and the space lists who is in it under a line such as "Managed by Launch Lead · Also Copywriter · Researcher." The practical effect is that a task goes to the specialist suited to it, and the specialist's own accumulated context stays attached to that agent rather than being lost between sessions. Routines are the part of Spaces that keeps working when nobody is watching. A routine is described as something that runs on a schedule — check-ins, follow-ups, reminders — and the space shows them in a Routines panel with their state and timing. The example space lists three routines: a "Launch morning brief" that runs daily at 9:00 AM, a "Friday recap" that runs Fridays at 4:00 PM, and a "Waitlist follow-up" that is currently paused. Each entry shows when it will next run or when it last ran. The point of routines, as the site frames it, is that the space does the standing work and pings someone only when it needs a decision, so recurring project chores like morning briefs, follow-ups, and weekly recaps stop depending on someone remembering to ask. Spaces does not sell AI usage. You connect the ChatGPT, Claude, or Gemini account you already pay for, and Spaces becomes the place those models do the work — you are buying the space, not another subscription for tokens. The Providers panel shows Anthropic's Claude and OpenAI's ChatGPT as connected, with Google's Gemini through Google AI Studio available to add, and Ollama available to run a model on the computer itself. Keys stay on the machine, and you can switch provider per agent at any time, so one specialist can run on one model and another on a different one. The site is explicit that Spaces is not a reseller: the AI account belongs to the user, and the app is the workspace that account operates in. The architecture is deliberately split between the local machine and the cloud. Each person's agents, API keys, and connected accounts stay on their own computer and never go to the cloud. What the cloud carries for a shared space is the space itself — the chats, files, and routines — and nothing else. Shared-space content is encrypted at rest with a per-space key held in Google Cloud KMS, and only members of the space receive that key. Anyone who uses Spaces alone can keep everything on their own machine, because a cloud space is only needed when other people have to work in the same project. The built-in browser lets agents use the real web rather than a cached or limited view of it. They can research, click through pages, and stay signed in, and because the browser lives inside the app, the agent sees the page the user sees. The site illustrates this with a research example: a Launch brief and a Supplier spec at example.com/solar-backpack, with the space's Files containing "Solar backpack — claims" listing a 65W peak panel, a 20,000 mAh pack, and a weatherproof shell, alongside an instruction not to claim the product "charges a laptop in an hour." The scenario shows an agent reading a live supplier page and checking its claims against the brief and the files already in the space. A space starts as yours. Inviting someone turns it into a shared space that both people work in: the same chats, the same files, the same routines, with their agents alongside yours. The site defines a cloud space as a project space that lives in the cloud instead of only on your machine, so other people can work in it — and notes that you do not need one to use Spaces by yourself. Pricing is split across three options. Spaces itself is free: the full desktop app, unlimited spaces on your computer, specialists, routines, and playbooks, and your own AI keys with no account needed. Spaces Cloud is the paid tier for teams, priced at $10.99 per year per person, described as $0.92 a month and, in the FAQ, as $1.49 a month, and it adds spaces that live in the cloud plus shared chats, files, and routines, and the ability to invite anyone who has a seat, while keys and agents stay local. Enterprise is a conversation rather than a checkout: Spaces inside your own cloud, data that never leaves it, and help with the whole setup. Everyone who works in a shared space needs their own seat, because each person runs their own agents, so a team of five is $55 a year in total; anyone who only works alone stays free, and cancelling leaves the desktop app free on your own computer. Getting started follows three steps the site lays out. First, download Spaces — it is free and no account is needed, and it installs like any other desktop app. Second, connect your AI by pasting the ChatGPT, Claude, or Gemini key you already have; one provider is enough to begin. Third, open a space: name the project, put a specialist to work, and the space starts holding the context. Spaces runs as a desktop app on Apple silicon Macs and Windows PCs. The examples on the site point to a few concrete ways teams use it. A product launch is run from a single Product Launch space, where a Launch Lead, Copywriter, and Researcher share a press list, waitlist copy, and checks of battery claims against the launch brief. Research work happens in the built-in browser, with agents reading live pages such as a supplier spec and comparing them against files in the space. Routine work is scheduled rather than requested: a morning brief at 9:00 AM, a Friday recap at 4:00 PM, and a waitlist follow-up that can be paused and resumed. Teams that need everything inside their own infrastructure can run Spaces inside their own cloud through the enterprise option. Taken together, Spaces is a place where a project's chats, files, and scheduled work live together, and where each person's AI agents contribute into that shared context instead of into private threads. It keeps the AI accounts and keys people already pay for, keeps their agents on their own machines, and charges only for bringing other people into the same space. The value proposition is simple: the team, not just the individual, gets to benefit from the AI work being done.
Cadenya is a hosted agent runtime that layers tools, agents, and objectives on top of the APIs you already run. It is explicitly not a framework you bolt into your application stack; instead, Cadenya runs the agentic loop for you. You connect your MCP servers, OpenAPI specs, and existing endpoints through a single tool layer that agents can use, then define an agent, shape its abilities, and run objectives against it. The product is positioned for developers and teams who want to bring agentic possibilities to life in software they already operate, testing safely and improving quickly without rebuilding the stack that supports them. The problem Cadenya addresses is the cost of adopting agentic capabilities inside a system that already works. Building agents typically means invasive change: framework glue inside the application, wrapper layers around APIs, and hand-built machinery for streaming, approvals, retries, context limits, and visibility into what an agent actually did. Cadenya's answer is a hosted loop that handles those concerns out of the box. The product states that it handles context compaction, tool approvals, webhooks and SSE streaming, embeddable widgets, and SDKs in four languages. A frequently asked question on the site answers directly that you do not need to rewrite your APIs: connect MCP servers, OpenAPI specs, and existing endpoints through a single tool layer, and agents can use them as they are. That preserves existing infrastructure investment while new agentic behaviour is layered on top, which matters because teams can iterate on agent behaviour without destabilising the services their business already depends on. The first layer is the tool layer, and the product's guidance is to start with your stack. You connect MCP servers, OpenAPI specs, and existing endpoints through a single tool layer agents can use. Because connection is based on specifications developers already know, existing services become available to agents as they are. Tools can be assigned to an agent individually, organised as tool sets, and combined with sub-agents. In the interface example shown on the site, a shipment-exceptions agent carries assignments such as a reroute shipment tool, an update ETA tool, a Dispatch API tool set, and a Customs Broker sub-agent, alongside memory layers such as a Carrier Playbook covering SLA policies. This layer is what lets an agent reach into real systems rather than only producing text. Experimentation is handled through variations. The interface shows a Default variation and Canary variations, each tied to a specific model, such as Anthropic Claude Sonnet as the default and OpenAI GPT-5.5 or GPT-5.2 as canaries, with a creation timestamp. A variation also carries its own system prompt, its own assignments, and its own memory layers. The product's stated goal for this area is to let you iterate without uprooting: swap models, evolve behaviours, and expand capabilities while retaining infrastructure. It also frames the runtime as a way to evolve with what is next, so you can adopt frontier models fast, test behaviours and compare approaches, and add functionality rather than complexity. Practically, that means model and behaviour changes are configuration inside the runtime rather than a rewrite of the surrounding application. Cost and context are managed through the token usage layer. Cadenya provides live token metering so teams can stay on top of costs, and describes reducing waste through progressive discovery, with efficiency improving as agents adapt. Progressive tool discovery can be enabled, with a maximum number of tools per search set between 1 and 10 (5 shown in the example), search hints of up to 5 terms, and a rerank threshold between 0.0 and 10 that can be left blank to skip reranking. The mechanism is described precisely: tool schemas stay out of the context window until the agent asks for them, and only names ride along, so every request gets smaller. That matters for agents with many connected tools, where schema bloat would otherwise consume context and budget before the agent does any work. Cadenya is also built to power real-time services. Webhooks and SSE push live updates, and the product states it makes it easy to wire agent events into your applications. The webhook delivery view on the site lists event types including assistant message, tool_result, tool_approval_requested, sub_agent_spawned, context_window_compacted, and timed_out, each with an HTTP status and target URL. Around this, the product provides tool approvals: approval-gated tools pause the agent and deliver a tool_approval_requested event so a person or system can approve before anything runs. It also ships embeddable widgets, described as a feature called Widgets that can be dropped into any frontend to enable agentic features like conversations and, per the product, so much more. Observation closes the loop. Cadenya is designed to let you monitor outcomes and understand how behaviours take shape in the real world, with the stated goal that clear visibility means your agents show their worth. Every objective keeps its trail: tool calls, webhook deliveries, token usage, and the feedback people leave on the outcome. The feedback view shows comments scored by sentiment, attributed to a variation and an objective, such as a reroute completed before an SLA breach, a customs hold caught with a proactive ETA update, a reroute that notified the recipient twice, a hold that occurred when a reroute was available, and a correctly escalated frozen-goods lane. Those scored outcomes are what make comparison between variations meaningful. Overall the product works as a hosted agentic loop in three steps: define an agent, shape abilities, and run objectives. Inference is model-agnostic. You point Cadenya at OpenRouter or any OpenAI-compatible endpoint and it uses that for inference. Each new account comes with $5 in credits on OpenRouter pre-configured; after that you provide your own LLM provider credentials. Agents can dispatch sub-agents, and the model configuration for sub-agents can be changed to be best suited for the job, which the product describes as giving the most efficient token usage and outcome. The runtime itself is described as unified: you add functionality, not complexity. The benefits follow from that design. Because the loop is hosted rather than embedded, teams can experiment safely and improve quickly. Because APIs are not rewritten, capabilities expand while infrastructure is retained. Because variations, memory layers, and assignments sit in configuration, frontier models can be adopted fast and behaviours compared rather than guessed at. Because events flow through webhooks and SSE, downstream services react immediately instead of polling. Because token metering and progressive tool discovery are built in, spend is visible and requests stay smaller. And because every objective keeps a trail of tool calls, webhook deliveries, token usage, and human feedback, improvement is grounded in recorded outcomes rather than impressions. Concrete scenarios appear throughout the product's own material. The site's worked example is a freight and logistics agent named for Meridian, a shipment-exceptions agent instructed to reroute a stalled delivery via a dispatch API, with assignments covering rerouting, ETA updates, a Dispatch API tool set, and a Customs Broker sub-agent, and memory layers holding a Carrier Playbook and SLA policies. Feedback entries describe rerouting before an SLA breach, catching a customs hold and updating the ETA proactively, and escalating a frozen-goods lane. Other described workflows include dropping Widgets into a frontend for conversations, pushing agent events into applications via webhooks and SSE so downstream services react immediately, gating tool calls behind approvals, and dispatching sub-agents for parts of a job. On integrations, Cadenya connects MCP servers, OpenAPI specs, and existing endpoints through a single tool layer, uses OpenRouter or any OpenAI-compatible endpoint for inference, and delivers webhooks and SSE streams to endpoints you provide. It ships SDKs in four languages and embeddable widgets for frontends. The site notes that each new account includes $5 in credits on OpenRouter pre-configured, after which you supply your own LLM provider credentials, and states that you can email support@cadenya.com to get a free month. Getting started is described as a few steps, the first of which is signing up, with sign-up available at app.cadenya.com and API documentation linked from the site. Cadenya's core promise is that you can bring agentic possibilities to life on the stack you already run. It converts agent development from a rebuild project into a hosted runtime you configure, connect, observe, and improve, with the surrounding concerns of context, approvals, streaming, cost, and feedback handled as part of the loop.
chat-recall is a tool that turns the conversation history your team has built up with AI coding assistants into a single searchable archive. It reads what your assistants have already written down — the chats, the plans, the task lists and the notes — and consolidates them into one searchable history. As the product puts it, Ctrl+F doesn't work on your brain, but now it works on your chat history: your team has done months of work with AI assistants, and none of it is searchable until chat-recall is installed. It is aimed at developers and teams who use several AI coding tools at once and want the record of that work to be findable rather than scattered. The problem chat-recall addresses is fragmentation. Claude Code, Codex, Cursor and OpenCode each keep a full record of the work you do with them, in its own format, and none of them can read the others. Across five AI coding tools that means months of chats, plans, task lists and notes sit in separate silos that no single search can reach. The result is work that gets redone because nobody can find the original decision, plus a quieter risk: passwords and API keys that were pasted into old conversations and never noticed again. Without a way to search across tools, both the value and the risk hidden in that history stay invisible to the team that created it. You start with a single command: npx chat-recall init. One command reads what your assistants already wrote and turns it into one searchable history. The steps the product describes are straightforward. First, your conversations: everything your assistants have written down, the chats, the plans, the task lists and the notes, across five AI coding tools. Second, passwords are removed on your own computer. Third, everything lands in one searchable history, available the moment it arrives. There is nothing to restructure by hand and no export to perform, because chat-recall reads the records the assistants have already created and assembles them into a single index. Privacy is handled locally rather than in the cloud. Passwords and secrets are removed before anything leaves your computer, and the vendor states that it only ever sees the last few characters of a removed value; the interface shows a masked preview such as the start of a token, a run of asterisks, and a short tail. Steps one through three of the pipeline happen on your own machine. That local-first design matters because the content being indexed is often the most sensitive material a team produces: live credentials, internal plans and unfinished work that nobody wants uploaded to a third-party search index just to make it findable later. Beyond organising history, chat-recall actively looks for secrets that leaked into old chats. Its security view groups every leaked credential by rule, and each row shows a masked preview of the value, a live-or-dead verdict on whether the key still works, the detectors that matched it, and how many sessions it appeared in, so you can see whether a key is still live and how widely it was exposed on one screen. The detectors know the key formats that the big services publish. If your company has invented its own format, you can tell chat-recall the pattern and it gets checked too, which is how custom internal credentials end up covered as well. chat-recall also gives your assistant access to the history itself. Your assistant searches it directly, so it stops asking what you decided last month, and one shared memory means decisions, findings and tasks live in one place rather than in a single tool's silo. The product lists tasks with proof, and what to fix next, among its outputs, and bugs turn into tasks on their own: each one shows up with the fix already sketched out, and closes itself once the problem is actually gone. Rules can be set once per project — mark a project a prototype or a live product, and every assistant that opens it plays by the right rules. Because teams rarely use one assistant on one machine, chat-recall includes a toolkit coverage matrix: six skills down the left and a column for each AI tool on each of two machines. A filled cell means the skill is installed there; an empty ring means it is missing, and clicking it copies the skill across with a sync-to-all action. Each row carries a coverage count so you can see at a glance which of your assistants has which add-on. The payoff is portability. A new laptop already knows everything once you sign in, with nothing to copy over by hand, and every add-on you have built up follows you to whichever assistant you pick up next, so trying a new assistant does not mean starting over. Overall, chat-recall works as a local assembly line rather than a cloud service. A single command reads the records that five AI coding tools have already written, strips passwords on your own machine, and produces one searchable history that is ready immediately. The same history, the same tools and the same rules are then available everywhere you work, across assistants and across computers. Three of the steps happen on your computer, and the product describes the flow as an assembly of your conversations, the removal of passwords, and one searchable history — with leaked passwords, what to fix next, tasks with proof and one shared memory all downstream of that assembly. The benefits follow from that design. Work stops being redone because the original decision is searchable, and your assistant stops asking about things you already decided. A new laptop is useful the moment you sign in, and a new assistant arrives with your accumulated add-ons already in place. Secrets stop being invisible, because leaked keys are surfaced with a verdict on whether they still work and how many conversations they turned up in. Bugs become tasks with a sketch of the fix and disappear on their own once resolved. And rules can be applied consistently, so a prototype and a live product are treated differently wherever they happen to be opened. Concrete uses follow the same pattern. A developer wants to find a decision the team made weeks ago and searches the shared history instead of scrolling through one tool's transcripts. A security-minded team runs the leaked-key check to catch credentials pasted into old chats and to see which of them are still live. Someone setting up a new laptop signs in and finds the whole history already there. A developer trying a new assistant keeps the add-ons built up over time. A bug found in a chat becomes a task with a sketched fix that closes when the problem is gone. And a project marked as a prototype is opened under the right rules by every assistant. chat-recall is built for developers and teams who work across multiple AI coding assistants and want a searchable record of that work. The named integrations are Claude Code, Codex, Cursor and OpenCode, with the site referring to five AI coding tools in total, and to add-ons or skills that can be installed per tool and per machine. Installation runs through npx chat-recall init, which places it in the Node.js command-line ecosystem, and the product links to documentation on how it works and on what your assistant can ask. The takeaway is that chat-recall gives a team back the searchability of its AI work. One command reads what Claude Code, Codex, Cursor and OpenCode already wrote, removes passwords before anything leaves your computer, and produces one searchable history that your assistant can search itself — while also catching keys that leaked into old chats and checking which of them still work.
Moji is a desktop application that opens Markdown files the way you would open a PDF. The project describes itself simply: double-click a Markdown file and start reading. Moji displays your document with clear typography, tables and diagrams, and keeps editing and export ready for when you need them. It runs on Windows, macOS and Linux, is free and open source, and requires no account. The product positions itself as a fast, clean and distraction-free way to read Markdown, with the stated purpose of making a Markdown file open the way a PDF does: with a double-click, instantly readable and with no setup required. On the project's site, the creator explains the motivation directly under the heading "why I built Moji": the goal was for a Markdown file to open the way a PDF does, with a double-click, instantly readable, with clean typography and no setup. The site adds that usability came first from day one, that Moji is lightweight and comfortable for long-form reading, and that it is simple enough to disappear while you read. Editing and export were added without losing that focus. The entire design intent is captured in the line "Less interface. More document." The problem being addressed is the friction between raw Markdown text and a comfortable reading experience: instead of configuring a toolchain or an authoring environment simply to look at a document, the user opens the file and reads it, and the more advanced capabilities only appear when they are actually needed. Opening and reading a document in Moji is deliberately flexible. The site lists four ways to open a file: a standard file dialog, drag and drop, file associations, and a multi-tab workspace. File associations mean a Markdown file can be opened by double-clicking it in the operating system, which is the behaviour the product is built around. The multi-tab workspace lets several documents stay open at once, so readers can move between files without returning to a file picker each time. Once a document is open, Moji renders a rich, secure preview that includes tables, task lists, footnotes, LaTeX, code highlighting, emoji and outline navigation. A synced outline keeps the document structure visible alongside the content, and a dark theme is available for focused reading. The interface uses subtle chrome and compact controls so that the content itself stays in the foreground. Editing is available but stays secondary to reading. Moji's editor is built on CodeMirror 6 and adds Markdown shortcuts, line numbers, history, and search and replace. The site describes this as precise editing covering code, shortcuts and search. The intent is that a user who opened a file to read it can switch into editing without leaving the reader or losing the distraction-free layout, then return to reading. Markdown shortcuts speed up common formatting tasks, line numbers help when working with code-heavy documents, history provides a record of changes within the session, and search and replace supports navigating and updating longer documents. Moji renders Mermaid diagrams without leaving the document. Valid Mermaid blocks in a file become responsive diagrams inline, and the site states that flowcharts, sequence diagrams, Gantt charts, class diagrams, ER diagrams and more are supported. Diagrams can be zoomed from 10% to 1000%, panned freely, or fit to view, and a minimap plus a dedicated diagram viewer help navigate larger charts. Each image can be exported individually as PNG. The rendered diagrams are described as self-contained SVG in HTML, PDF and PNG output. The site summarises this as turning code into diagrams instantly, which means a document that describes a system in Mermaid syntax can be read and understood visually in the same window. Exports are described as predictable. Moji can export to PDF for precise layouts, to HTML for web-ready output, and to PNG, which the site presents as suited to long documents. Exports preserve typography, diagrams and very long documents. The export dialog is intentionally simple: the user chooses a format and a layout. Because Mermaid output is embedded as self-contained SVG, diagrams that appear in the reader also appear correctly in the exported PDF, HTML or PNG file. Moji is a desktop application rather than a web service. Official installers for Windows, macOS and Linux are published directly through GitHub Releases. For Windows, an NSIS installer is provided for Windows x64 with automatic updates. For macOS, a universal DMG supports both Apple Silicon and Intel, with manual updates. For Linux, users can choose an AppImage with automatic updates or a DEB package for manual installation. The version referenced on the site is 1.0.7. Moji is free, distributed under the MIT license, and requires no account, so no sign-in or subscription stands between the user and their document. The stated benefits follow from that design. Because Markdown files open with a double-click, the tool fits into the same habit as opening a PDF. Because the interface is lightweight and uses subtle chrome, it is described as comfortable for long-form reading and simple enough to disappear while you read. Because the preview supports tables, tasks, footnotes, LaTeX, code highlighting, emoji and diagrams, technical and structured documents render correctly rather than as raw text. Because editing and export are built in, a single application covers reading, light authoring and sharing. And because the interface is available in Portuguese, English, Spanish, Japanese, Chinese and Russian, readers can use the product in their own language. Several concrete scenarios follow from the features the site describes. A developer who receives a README or technical document can double-click the file and read it immediately with code highlighting and correctly rendered tables. A writer or note-taker can open a long Markdown document in the multi-tab workspace, follow the synced outline, and use the dark theme for extended reading sessions. Anyone working with technical documentation can embed Mermaid blocks and view flowcharts, sequence diagrams, Gantt charts, class diagrams or ER diagrams inline, zoom into a detail and export a single diagram as PNG. A user who needs to distribute a document can export it to PDF with precise layouts, to HTML for the web, or to PNG for long documents, with typography and diagrams preserved. Someone who needs to make a small correction can switch into the CodeMirror-based editor, use Markdown shortcuts and search and replace, then return to reading. And because Windows, macOS and Linux builds are all available, the same reader can be used across different machines. Moji is aimed at people who read Markdown on a desktop and want it to behave like a document rather than source code: developers reading repository documentation, writers and note-takers working in Markdown, and anyone who deals with technical documentation that contains diagrams. It is explicitly free, open source under the MIT license, and requires no account. The site states that the source can be explored, development followed, issues reported, and the project's next chapter shaped through the public repository. Technically, the site names CodeMirror 6 as the editor foundation and Mermaid for diagram rendering. Moji runs on Windows, macOS and Linux, with distribution handled through GitHub Releases, and the interface is localised into six languages. Moji's primary value proposition is stated on its own homepage: opening Markdown should be as simple as opening a PDF. It delivers that by pairing a focused, lightweight desktop reader with the editing, diagram rendering and export tools needed when reading is not enough, and it does so free of charge, as open source, across Windows, macOS and Linux.
Devin Voice is a voice mode feature that allows you to talk naturally with Devin, Cognition's AI software engineer. It is designed for software developers and technical users who want to explore ideas, pressure-test approaches, and hand off work while away from their keyboard. Instead of typing, you can speak a task out loud, and Devin will plan, code, and deliver the work. The feature is accessed through the Devin web application, where you can start a voice call in Agent mode or within an existing session. This creates a hands-free way to interact with an AI software engineer, making it possible to continue development work even when you are not at your desk. The problem Devin Voice addresses is the friction of being tied to a keyboard when you need to delegate coding tasks or discuss technical ideas. Developers often have moments of inspiration or need to hand off work while away from their computer—whether commuting, walking, or simply taking a break. Typing is not always convenient or possible. Voice mode solves this by enabling natural spoken conversation with Devin. It matters because it removes the keyboard as a barrier to starting and managing development work. You can capture ideas as they come, verbally describe a task, and let Devin handle the implementation, all without sitting down to type. This makes the development process more fluid and accessible. One of the core features of Devin Voice is the ability to start a call easily. On the home page in Agent mode, or in an existing session, you click the voice call button beside the message box and allow microphone access. Any message you have already typed is sent when you start the call, so you do not lose your drafted context. This seamless transition from typing to talking ensures you can switch modes without interrupting your workflow. The voice call button is indicated by a waveform icon, and hovering over it shows the 'Start voice call' tooltip, making the entry point clear and discoverable. Once a call is active, you have several controls to manage the conversation. You can mute your microphone to pause your input, then unmute to speak again. If you are muted but need to say something quickly without toggling mute, you can hold the Space bar to talk, which is especially useful when you are not typing. You can also silence Devin, which turns off Devin's audio without muting your microphone, allowing you to continue speaking without hearing responses; clicking 'Unsilence Devin' restores the audio. To end the call, you simply click 'End voice call'. These controls give you fine-grained command over the voice interaction, making it adaptable to different situations. While a voice call is ongoing, you can navigate within Devin and the call stays connected. This means you can move between different views, check code, or review other parts of the application without ending the conversation. Your spoken conversation appears in the session history, so you can refer back to it later. The documentation also offers tips to make the most of voice mode. You are encouraged to interrupt Devin's work, ask questions, and clarify your thoughts as they occur. You can even interrupt Devin while it is talking, which keeps the conversation natural and dynamic. Additionally, if you have preferences for how Devin should speak—such as speaking faster or slower, or a particular communication style—you can simply ask, and Devin will adjust. The unique approach of Devin Voice lies in its integration with Devin's underlying AI capabilities. According to the product description, it is powered by GPT-Live for natural conversation, with Cognition's new SWE-2 coding model under the hood. This combination allows Devin to understand spoken tasks and translate them into planned, coded, and delivered work. The voice interface is not just a dictation tool; it is a full conversational interface with an AI software engineer that can act on your requests. This means you can speak a task and have Devin ship it, as the tagline says: 'You say it, Devin ships it.' The benefits for users are significant. You gain the ability to work hands-free, which is useful when you are away from your keyboard or occupied with other tasks. You can explore ideas and pressure-test approaches through natural conversation, which can be faster and more expressive than typing. Handing off work becomes as simple as speaking a task, and Devin takes care of the planning, coding, and delivery. The ability to interrupt and ask questions means you stay in control and can clarify details in real time. The session history provides a record of your conversations, and the speech preference adjustments let you customize the interaction to your liking. Overall, Devin Voice makes interacting with an AI software engineer more accessible and flexible. Concrete use cases for Devin Voice include exploring ideas when you are away from your desk, such as during a walk or commute. You can talk through a feature concept, and Devin can help refine it. Pressure-testing an approach is another scenario: you can verbally discuss trade-offs and edge cases, and Devin can respond with analysis or suggestions. Handing off work is a primary use case—speak a coding task out loud, and Devin will plan, code, and deliver it while you continue with other activities. You can also use voice mode to ask questions and clarify thoughts as they arise, interrupting Devin's work or speech to get immediate answers. Navigating within Devin during a call lets you review code or check other sessions without breaking the conversation. Finally, adjusting speech preferences allows you to tailor Devin's voice to your needs. Devin Voice is targeted at software developers, engineers, and technical teams who use Devin. It is particularly suited for those who want to hand off coding work or explore ideas while away from their keyboard. The feature is available within the Devin web application, and no additional integrations are mentioned in the documentation. The tech stack includes GPT-Live for conversation and SWE-2 for coding. Pricing and plan details are not specified in the provided content, but the product is part of the Devin platform, which can be tried at https://devin.ai. In summary, Devin Voice is a voice mode for Devin that lets you talk naturally with an AI software engineer to explore ideas, pressure-test approaches, and hand off work. With easy call initiation, flexible call controls, and the power of GPT-Live and SWE-2, it enables hands-free development and natural conversation. Whether you are away from your keyboard or simply prefer speaking over typing, Devin Voice makes it possible to ship work by speaking it out loud.
EasySpecs is a Spec Engineering Assistant and spec review platform built for teams practicing Spec-Driven Development. It documents undocumented codebases and turns them into trustworthy specifications, grounds AI agents in reality, and lets teams create Trust by Design Specs before code is written rather than after bugs pile up. The product is aimed at product owners, developers, technical product managers, and organization leaders who need product and engineering to share the same source of truth about an application. Its stated purpose is to close the gap between teams shipping at 1.5x and teams shipping at 100x — a gap EasySpecs describes as trust rather than speed. AI is accelerating how fast software changes. As EasySpecs frames it, AI agents generate code 100x faster than humans write it, but a team cannot review it all. The result is familiar to engineering leads and CTOs: “Agents generate faster than my team can review. We're drowning in AI PRs.” Developers describe the other side of the same problem: “I babysit the agent the whole run. If I look away, things go wrong.” Spec Driven Development is presented as the new standard, and with it come new consequences — a need for a tool for quality specs, new spec management requirements, documentation that lags, shared context that goes stale, overlap between product and engineering with no place to align together, and surging merge requests that create code review bottlenecks. EasySpecs positions itself as the response to each of those consequences. Step one of the EasySpecs workflow is understanding the code. EasySpecs produces functional documentation of your project with up to 98% LOC coverage assignment. That documentation is described as the first stone of Trust Engineering and as a foundation: functional documentation of the real system, so every later Spec starts from how the app actually behaves rather than from a guess. The stated benefit is that change requests start aware of real behavior and user intent, which lets teams make informed decisions instead of building on assumptions. Because the documentation reflects the actual application rather than someone's memory of it, it gives the rest of the workflow something concrete to build on — the factual baseline that intent and Specs are later grounded against. Without that baseline, every downstream step would inherit the same uncertainty the documentation exists to remove. Step two is polishing the intent and grounding it to the current codebase. When intent is fuzzy, EasySpecs helps you craft, clarify, and ground it to the current codebase before agents generate code, so that Spec-Driven Development has something trustworthy to drive. Intent is described as the ask behind the change — captured and grounded in how the app actually works, so product and engineering share one picture before Specs are written. The product surfaces this through a Specs accordion that shows Change, Intent, Diagram, and Spec steps. For technical product managers, the outcome is specs that developers can ship against, ready the moment engineering picks them up and integrated with Jira. This step matters because fuzzy stories burn engineering time: when the ask behind a change is unclear or divorced from the real system, developers end up interpreting rather than building. Step three is creating Trust by Design Specs. Once intent is clear, EasySpecs creates Trust by Design Specs with structured views and HTML-rendered views, so the change is visible and checkable before you write the code. Every Spec is sided by a Trust Spec. The Spec is the Spec-Driven Development Spec — what to build — presented in structured and HTML views the team can actually read. The Trust Spec holds validators, evals, and checks that sit beside the Spec, so you know how you will trust the change before agents generate code. The Product Hunt description also refers to reviewing specs including Oracles and Rubrics, and to developers working from Specs and Spec of Trust validators. The underlying principle is Trust by Design: define how you will trust the code before you write it, not after bugs pile up — and the sooner you set that bar, the less cost and fewer problems you carry. EasySpecs works in three steps and frames the whole loop as Trust Engineering: understand the code, polish the intent and ground it to the current codebase, then create Trust by Design Specs — before you ship, not after bugs pile up. Around that loop, EasySpecs acts as spec-driven change management. A dashboard shows change requests and linked Spec status across projects, so every change request and its Spec can be tracked. Documentation auto-syncs, addressing the problem of docs that lag and shared context that goes stale. EasySpecs also presents itself as one Spec-Driven operating system for tech and product, introducing Spec-Driven Development across a team so product and engineering share the same source of truth. The stated aim is that the gap between teams operating at 1.5x and teams operating at 100x is not speed but trust, and that grounding agents in reality lets you scale agentic development without babysitting, documenting the foundation once. EasySpecs states a set of outcomes tied to each consequence of faster shipping. Where a tool for quality specs is needed, EasySpecs creates quality specs easily. Where shipping faster demands new spec management, EasySpecs provides spec-driven change management. Where docs lag and shared context goes stale, EasySpecs auto-syncs documentation. Where product and engineering overlap with no place to align, EasySpecs offers one place to align together. Where merge requests surge and code review bottlenecks form, Trust Engineering eases merge requests, making spec review the new merge request review. For developers, the promise is concrete: stop babysitting the agent, and start generation from clear intent and checks rather than vibes. For product owners, change requests can be grounded in the real app and shaped into Trust by Design Specs the team can actually see. One testimonial sums it up: “My team finally speaks the same language about specs. The pace of change was so fast we could not align. Now with EasySpecs, all clear.” Concrete scenarios appear throughout the content. A team working with an undocumented codebase can have EasySpecs produce functional documentation first, so change requests start aware of real behavior. A developer struggling with AI-generated pull requests can move review upstream to Specs and Spec of Trust validators instead of reviewing an endless stream of code. A technical product manager can write a spec against the system that developers can ship against, ready the moment engineering picks it up through Jira. A product owner can ground a change request in the real application, polish the intent behind it, and shape Trust by Design Specs the team can see. An organization leader can introduce Spec-Driven Development across a team so product and engineering work from the same source of truth, with a dashboard tracking every change request and its linked Spec across projects. And a developer working in an editor can stay inside VS Code, Cursor, Antigravity, or any VS Code-compatible IDE while the workflow runs. EasySpecs is built for product owners and developers — two sides of the same Trust by Design loop — and also speaks directly to technical product managers and organization leaders. The workflow is integrated with Jira and Linear, and with VS Code, Cursor, Antigravity, and any VS Code-compatible IDE on the development side. The product is listed on Product Hunt under SaaS, Developer Tools, and Development, and the site includes a section on agentic coding noting Loop Engineering, Graph Engineering, Context Management, and more. No pricing or plan details are stated in the available content. EasySpecs positions Trust Engineering at the center of AI-accelerated software delivery: document the real system once, polish intent against that reality, then define how you will trust the change before agents generate code. By turning Specs — including their validators, evals, checks, and the review of Oracles and Rubrics — into something reviewable, it reframes spec review as the new merge request review and gives product and engineering one shared source of truth. The takeaway is straightforward: the gap between 1.5x and 100x is not speed, it is trust, and EasySpecs is built to supply it.
Raycast 2.0 is the next generation of Raycast, described by its creators as a new foundation, redesigned from the inside out. It is a launcher for macOS that acts as your shortcut to everything, and this release adds AI that can take action across your apps, Automations for recurring tasks, and Projects to keep ongoing work together. It is built for Mac users who want to move quickly across their desktop, combining a command launcher, file search, dictation, and an AI chat experience in one place. Raycast 2.0 is available to download now, and it requires macOS Tahoe and Apple Silicon. The update exists because Raycast chose to rebuild its launcher on a new foundation rather than continue patching the previous one. Raycast V1 was already described as the best Launcher, so the bar for 2.0 was high: the goal was to keep what people relied on while modernizing the interface, the hotkey handling, and the AI experience. That scope comes with trade-offs the team states openly. Raycast calls this a major update and tells users to expect frequent updates and occasional rough edges. On installation, the new Raycast will replace Raycast V1; there are just a handful of missing features, and those will be added soon. The most concrete risk of any rebuild is losing a carefully tuned setup, so 2.0 is designed to bring your configuration with you instead of asking you to start over from scratch. The core of the new release is a reworked AI experience built around two surfaces. Quick AI keeps you in the same Tab while adding more power, so you do not have to leave what you are doing to get an answer. AI Chat collects skills, agents, and memory in one place, giving AI work a persistent home inside the launcher rather than a one-off query box. Beyond answering questions, the AI in Raycast 2.0 can take action across your apps, which means the assistant is not limited to conversation alone. You can also connect your own ChatGPT or Claude account and put AI to work alongside the commands and extensions you use every day. Connecting your own account lets the AI you already use become part of the same surface as your launcher commands and extensions. Several changes target the everyday speed of the launcher itself. File Search now sits in Root, described as one less step to find your files, so files are reachable from the top level of your search instead of requiring extra navigation. File search is also faster in v2, and the FAQ lists quicklinks and snippets tagging among the additions in this release. Because root search is where queries begin, moving File Search there shortens the path between thinking of a file and opening it. Quicklinks and snippet tagging build on the same idea: your own shortcuts, snippets, and saved searches stay close at hand rather than buried inside menus, which matters for people who run the same workflows dozens of times a day. The look and feel of Raycast has been updated to feel right at home on macOS Tahoe, giving the v2 interface a refreshed appearance. The release also adds built-in dictation under the banner Type with your voice, so text can be entered by speaking without leaving the launcher. Settings have been reorganized, making the growing set of options easier to navigate as the product adds capabilities. In addition, you can configure inline, meaning hotkeys and aliases can be assigned directly from root search. That combination matters because a launcher is only fast when it is configured the way you actually work, and v2 places configuration and voice input on the same surface as search. Raycast 2.0 also treats the upgrade itself as a migration rather than a fresh start. During onboarding you are prompted to import or migrate your data from Raycast v1, and if you skip the step or want to rerun it later, you can use the documented commands. The Migrate from Raycast v1 command automatically migrates your data, and importing settings at this step also imports and migrates all of your shortcuts to Raycast v2 and disables them from working in Raycast v1, ensuring that your hotkeys do not conflict. Raycast calls this the recommended approach because some extra data is not imported with the manual route, including Clipboard History, Wrapped, and the Emoji picker. The alternative, Import Settings and Data, is a manual import from a .rayconfig file that you must export from Raycast v1. For people who build their own tools, custom extensions also need attention: to import correctly you must be on the latest version of Raycast V1, or at least v1.104.16, and if they did not import automatically you can run npx @raycast/api@latest dev, which is designed to pick up the new version if it is running. The benefits of Raycast 2.0 come from combining a faster launcher with AI that can act. Files are reachable in fewer steps, AI answers arrive in the same Tab through Quick AI, and longer AI work has a dedicated place in AI Chat with skills, agents, and memory. Built-in dictation offers a different way to input text when typing is slower than speaking. Because hotkeys and aliases can be assigned from root search and your existing shortcuts migrate from v1, the muscle memory you have already built keeps working in the new version. The result is a launcher that feels familiar on day one while offering more capability than before, which is exactly the balance the team set out to strike with a major rebuild. In practice, that shows up in concrete workflows. You can type a file name and open the result directly from root search instead of navigating to a separate file search view. You can ask Quick AI a question in the same Tab while staying in the flow of your current task, or move into AI Chat when a task needs skills, agents, and memory to be sustained over time. Ongoing work can be kept together in Projects, while Automations handle recurring tasks so they do not have to be triggered by hand. When writing is faster by voice, built-in dictation takes over, and when you want a command at your fingertips you can assign a hotkey or alias to it straight from root search. Upgrading is itself a workflow: import during onboarding, or run the migration commands afterwards. Raycast 2.0 is aimed at Mac users, and specifically at anyone running macOS Tahoe on Apple Silicon, since those are the stated minimum requirements. People already using Raycast V1 are the primary audience for the upgrade, and that includes extension developers, who need to be on Raycast V1 v1.104.16 or later for custom extensions to import correctly, or run the Raycast API dev command if they did not. Alongside the free download, Raycast offers Pro, Teams, and Enterprise plans, with pricing published on the Raycast site, plus an iOS app, a Windows page, and a browser extension in the wider Raycast family of products. AI in v2 can be used with your own ChatGPT or Claude account, letting you bring an existing AI subscription into the launcher. Taken together, Raycast 2.0 is a rebuild of a launcher that many Mac users already depend on, and the site sums it up directly: the launcher, relaunched. A new foundation, redesigned from the inside out, with AI that can take action across your apps, Automations for recurring tasks, Projects that keep ongoing work together, AI Chat with skills, agents, and memory, built-in dictation, faster file search in root, and a migration path that carries your settings, shortcuts, and extensions forward. The value proposition is the combination: more capability in the same fast surface you already know, without asking you to rebuild your setup from zero.
Thousand is a documentation engineering platform that stores docs as plain markdown in a git repository, then shapes a separate workspace for every reader based on their permissions. It is built for teams whose documentation must serve two very different audiences at once: people who want to read and edit a normal document, and AI agents that need clean markdown they can parse. Every folder in the repo knows its own audience, so a teammate, an outside guest with no account, or a bot with a token each sees exactly the folders their rules name and nothing else. The company describes it as git-backed docs for humans and agents, in one markdown repo with folder-level access. The problem Thousand addresses is that markdown is the document, yet markdown alone could not do certain things. Git shares an entire repository all or nothing, so there was no straightforward way to give one person finance and another person design while keeping everything else hidden, especially for readers who never touch git. Teams ended up copying the same content into a separate document tool, which created a second copy that could drift from the source of truth. Meanwhile agents needed machine-readable text, and the pages built for human eyes had to be scraped, converted, and exported. Thousand's premise is that one file should be first-class for both readers: your agent reads the markdown, your teammate reads the same bytes as a document, with no export and no second copy to drift. Path-level access control is the core of the product. Instead of sharing a repo all or nothing, each folder in Thousand has its own rules. In the example shown on the site, admins get write access, the design folder is writable by one person, the notes folder is readable by a summarizer bot, the marketing folder is readable by the team, and when no rule matches a folder it stays hidden. These rules are enforced even for readers who never touch git, so access is real rather than convention. Access can be extended outside the company too: any document becomes a link that opens in the browser for someone with no account, with no sign-up required on their side and a revoke button on yours, plus a record of how many times the link was opened. Half of a team will never open a terminal, and the site acknowledges that directly: for them it is just a document. Thousand lets people edit markdown without seeing markdown, writing in a document editor while the file on disk stays clean markdown and every save becomes a named commit. Comments follow the same philosophy: threads live beside the document rather than inside it, and they are saved as markdown that a clone carries too, so feedback never pollutes the file's content. Decks and PDFs live in the workspace as well, rendering in place and remaining searchable by what they say rather than only by filename. Drawings and tables are not excluded either: an Excalidraw canvas or a CSV table is simply a file in the repo, so it opens ready to edit under the same access rules as everything else. Agents are treated as members of the workspace, not add-ons. A bot gets its own name, its own folders, and a token that always expires; the profile card acts as the preflight, showing exactly what the agent can read and what stays hidden. The workspace also tidies itself: duplicates, stale drafts, and dead ends get swept out on a schedule, and nothing is removed without a human merging it. Thousand keeps company with existing remotes, described as one more remote on the same repo, so it works alongside GitHub or GitLab without a conflict, with one repository and three remotes and nothing moving. Finally, agents get their own copy of a page: when asked for markdown, the site answers markdown, so agents can read a dedicated file instead of scraping a page built for eyes. The overall approach is that documentation lives once, in markdown, inside a git repository that the customer owns, and everything else is a filtered view on top of it. Each repo's docs folder mirrors into the workspace on every commit, read-only and stamped with its source, so nobody maintains a copy and it never rots. A reader's workspace is then shaped exactly like their permissions: the same underlying bytes produce a document view for a person, a markdown response for an agent, and a hidden folder for anyone whose rules do not name it. Commits are the unit of change, whether they come from a developer pushing to a remote, a colleague saving in the document editor, or a bot signing in by token. The site summarizes this as documentation engineering: plain markdown, real boundaries, one clone to leave. The stated benefits follow from that design. Access is real rather than conventional, so sensitive folders such as finance can stay hidden from people and agents who should not see them, even in history. Context from every codebase can be synced into one place, meaning documentation stays current instead of rotting in separate tools. Non-technical teammates can contribute without learning markdown or git, because the editor hides the syntax while preserving the file. Feedback arrives as comments that never touch the file, so a clone carries the threads without altering the document itself. Because the repo is yours and exit is a single git clone, adoption carries no lock-in: after cloning, you have the same files, the same history, and the same markdown. Pricing is described as nothing while Thousand is early. Concrete scenarios from the site include a team writing a Q3 budget in a finance folder, where the numbers table, renewals list, and next steps are read as a document by colleagues while an agent reads the same bytes as markdown. An engineering org can mirror each repo's docs folder into the workspace on every commit so API server and web app context sit in one place. A designer or operations person can edit a pricing page in the document editor while a thread about whether it holds if trials convert slower stays beside the file. Someone can share a Q3 deck with an outsider as a link that opens in a browser with no account, track that it was opened three times, and revoke it later. Agents can sign in with an expiring token to read only the notes they are allowed, and an office deck, a PDF, an Excalidraw diagram, or a CSV of vendors can all be stored, rendered, and searched alongside prose. Thousand is aimed at teams working in what the site calls a post-AI workforce, where colleagues and agents share the same documentation. That includes technical teams who already keep markdown repos and want real access boundaries, and non-technical teammates who will never open a terminal but still need to read and edit the same files. Outside collaborators and guests are supported through links that require no account. Integrations are framed as remotes: Thousand is one more remote on an existing repository and works alongside GitHub or GitLab. The stack is deliberately plain, built around markdown and git, with Excalidraw drawings and CSV tables stored as ordinary files. Getting started is a Google sign-in, and an existing markdown repo can be added as a git remote and pushed, history and all. Pricing is nothing while Thousand is early, and git clone is always the complete exit. Thousand's value proposition is that documentation can serve humans and agents from a single markdown repository while still enforcing real boundaries. One file works for both readers, each folder knows its audience, every save is a named commit, and agents participate as members with expiring tokens. Because the repo belongs to the team and clones out cleanly, the platform adds structure and access control without locking anyone in. Documentation engineering, as Thousand frames it, is plain markdown, real boundaries, and one clone to leave.
Modeinspect is a production-grade AI design tool that runs in your codebase, offering a design canvas with your codebase and coding agents built in. It is where teams design high-fidelity features directly on the real product, using actual components, tokens, live data, states, and breakpoints. The canvas sits on top of your live product, allowing designers and engineers to work in one unified loop. Modeinspect is built for design engineers and product teams who want to skip the traditional handoff and design with production fidelity from the start. The platform integrates your codebase, the canvas, and coding agents out of the box, with no MCP servers, no localhost, and no devops glue required. Most software is designed twice: a picture first, then again in code. This two-step process causes intent to drift between design and implementation. Designers create mockups in tools like Figma, engineers later reinterpret them in code, and every change restarts the loop. The old handoff chain can take 45+ days from design to ship, involving specs, redlines, and repeated rebuilds. Modeinspect addresses this by letting teams design on the real thing. The canvas is not the destination; the product is. By designing directly in the codebase, teams avoid throwaway mockups and ensure that what they design is what ships. The platform offers a unified, collaborative design environment in code. Everything is in one place: your codebase, the canvas, and coding agents are integrated out of the box. There are no MCP servers, no localhost, and no devops glue to manage. This means teams can start designing immediately without complex setup. The canvas supports live product capture: you can grab any element of your live product onto the canvas, pixel-perfect and fully editable. This lets you start from the real state of the product rather than from scratch. Controls, not prompts, is another core principle: a padding change shouldn't take a paragraph. You can edit anything, on canvas or in code, with the visual controls you already know. Modeinspect is built for design engineers, providing components 1:1. You can drop in the actual components your product ships, with every variant and every state intact, so you never work with a redrawn look-alike that quietly drifts from the real thing. Tokens are enforced: every color, space, and text style comes straight from your library, so everything you place is automatically on-brand, and nothing off-system can sneak in. Breakpoints are native: you can lay out mobile, tablet, and desktop side by side and watch each one reflow live, never relying on a frozen frame that might not survive on a phone. Dynamic states are supported: hover, focus, error, empty, loading, and success can be shaped on the real component, so your design never falls apart when someone actually uses it. AI exploration is integrated: you can use the latest AI models to explore variants, restyle a section, adjust copy, or apply a design direction while you stay in control. You can build straight in code: every move you make on the canvas becomes the real product as you make it, with no redlines, no spec docs, and no waiting on a rebuild. Real data and real flows are central: you design on top of live data and real journeys, including long names, empty states, and the messy edge cases, so your work holds up in the wild, not just in a tidy mockup. Capture to canvas allows you to spot something in the real product you want to rework and pull it straight onto the canvas, pixel-exact and fully live, to start from where things actually are. The output is code your engineers want to merge. Mode reads your file layout, components, tokens, conventions, and existing logic, then writes within them. Pull requests land scoped, type-safe, and ready for engineering review. The system produces scoped, clean diffs so engineering reviews focused changes, not rewritten surface area or noisy AI churn. Your design system is enforced: Mode pulls from your component library and design tokens, with no hardcoded colors, no magic numbers, and no throwaway components. There is no generated UI debt: changes reuse your components, tokens, utilities, and styling system instead of creating a parallel design system. Changes are type-safe: props, state, events, and data shape are checked against the product instead of guessed from a mockup. The collaborative workflow is seamless. There is no localhost to share and no branches to wrangle. You can send a link, get comments, and open the PR in one click. This enables a production loop where prototyping, design QA, and shipping PRs happen in one place. For prototyping, you can create prototypes that feel like the product with real data, dynamic states, breakpoints, and interactions, so you pitch with the thing rather than mockups. For design QA, you can compare canvas to live build pixel-by-pixel, spot drift, fix it, and keep moving, with no round-trips through Figma. For shipping PRs, you can push minor visual changes or new components as merge-ready PRs with context, screenshots, and a clean diff. The benefits are measurable. According to a story from Prelude, a team that merged design and engineering in the same loop saved 22 days on their delivery cycle, reduced engineering handoffs to 0, and removed design QA as a separate step. This represents a 4.5× speed improvement, with design to ship in about 10 days instead of 45+ days. Testimonials highlight that designers explore on the actual codebase, with real data, and open the PR themselves, going from idea to a merged PR without a handoff. Teams like Kiwi.com, Moss, and NCCER report that Modeinspect integrates seamlessly with their codebase and design system, enables iteration on top of a complex product, respects their design system 1:1, and allows real-time changes pushed directly to code for senior developers to review and merge. Modeinspect is available as a web application optimized for larger screens. Pricing includes a free tier at $0/mo and paid plans at $24/mo and $48/mo. The Product Hunt listing also mentions "99 Days Free AI Credits." The platform is designed for design engineers, product designers, and cross-functional product teams who want to work in code. It is used by teams at Prelude, Kiwi.com, Moss, NCCER, and others listed as customers. In summary, Modeinspect is the production-grade AI design tool that runs in your codebase. It unifies design and engineering in one loop, letting teams design high-fidelity features with real components, tokens, live data, states, and breakpoints, then ship them as clean, scoped, type-safe code diffs. By eliminating handoffs and mockup drift, Modeinspect helps teams ship faster and with higher fidelity. The canvas is not the destination; the product is.