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Writing AI Tools
Discover and compare the best writing AI tools and software. Browse 128+ curated tools with reviews and rankings.
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Superhuman Go is an AI assistant and agent platform that works everywhere you already work. Rather than waiting for a prompt, Go looks for opportunities to help you say something better or do something faster, offering proactive suggestions inline across more than 1 million apps and websites. It runs inside Gmail, Slack, your documents, and your browser, and it can also be used through a dedicated Go app for focused work with AI. The product is built for people who want AI help in the flow of what they are already doing, and it is designed to fit into an existing workflow rather than replacing it. Superhuman describes Go as an AI assistant that moves with you to save time, improve focus, and make work effortless. The problem Go addresses is that most AI assistants are destinations. The Superhuman Go FAQ frames this directly: tools like ChatGPT, Claude, Cowork, and Gemini are powerful general-purpose chatbots, but you have to open a separate window, describe what you need, and copy the result back into your work. That means switching tabs, re-explaining context, and pasting answers back and forth. Go is built for the opposite experience. Because it lives inside the apps where you already write and work, it already understands the email you are replying to, the document you are drafting, or the message you are about to send, so no prompt engineering is required. Instead of waiting for a request, Go surfaces the right suggestion at the right moment, the way Grammarly's underlines always have. The result, according to Superhuman, is depth where it matters most: drafting, rewriting, replying, and getting tone right across every app you use. Suggestions as you type. Everywhere you write, Go annotates in real time and points out where you could be clearer, more convincing, or simply wrong. It can give you the knowledge you need as you type, whether that is a grammatical rule, a company-approved stat, or a forgotten tidbit from last week's meeting. The writing intelligence comes from a customizable Grammarly agent: you tell it which underlines you find useful and it adjusts its feedback going forward. A separate Knowledge Checker agent acts as a researcher behind the scenes, searching your data sources for evidence to back up your claims and preventing you from making the wrong one. Managing email and meetings. Go sorts your incoming mail and pre-drafts replies in your voice and with your context, so you can respond in record time. That work is handled by the Email Assistant agent, which is informed by your voice, calendar, and connected apps and is powered by Superhuman Mail. Go also serves as a meeting co-host: you can ask it to handle the prep and the follow-through, having it crowdsource the agenda before a team sync or auto-assign tasks when the meeting ends. A team of agents. Go comes with a powerful set of default agents and lets you build your own. The Agent Store is a growing library of ready-made agents from Superhuman and its partners, with agents for schoolwork, sales teams, accountants, and more. A Daily Brief agent starts every day with a custom report based on your apps, showing deals about to close in Salesforce, Jira tickets from last night, and tasks and meetings that need your attention. Custom agents are designed for your own workflows and routines, and can be configured to show up as you type, on a schedule, or after an event, such as when a meeting ends or someone sends you an email. Agents are described as your team of AI collaborators, built for common tasks that fill up your workday, like summarizing long threads, drafting reports, or finding information across tools. No coding required. Anyone on the Pro, Business, or Enterprise plans can build custom agents through a simple chat conversation or a visual agent builder, and agents can be shared directly with teammates or published to a company agent directory. For deeper integrations, the Superhuman Agents SDK lets developers build agents that plug into an organization's unique tools, systems, and workflows. Connectors and context. AI is only as good as the context you give it, so Go connects to the apps you use most through pre-built or custom MCP connectors. Everything Go says and does is grounded in your knowledge, which is what allows it to take on more complex tasks, such as building out a team project tracker and keeping it up to date. Superhuman calls this your second brain: the combination of connected data sources and agents that makes the assistance genuinely useful rather than generic. Go changes shape to meet you where you are and conform to how you work. In your text it appears as an underline that delivers the knowledge you need as you type. A context-aware side chat is always at your side, ready to chat about whatever you are reading, writing, or wondering. On your cursor, highlighting text in any app cues a list of actions from your favorite agents. In Slack, you can message Go like a teammate or at-mention it in a group channel for a quick answer or task. Because Go understands what is on your screen, you never have to copy-paste or describe what you are working on. The standalone Go app is the home base for tackling complex tasks and managing all of your Go agents. The intended outcome is time saved, improved focus, and work that feels effortless. Superhuman positions Go as a practical AI work assistant that reduces your daily admin tasks, and it states that nothing happens without your approval: Go suggests, and you decide. On trust, Superhuman maintains Grammarly's 17-year track record of safe, responsible AI, and its infrastructure is trusted by over 40 million people. The company follows industry best practices in security and privacy, you control whether your content is used to train Go's AI models, and for Enterprise users AI training is off by default. Use cases include drafting and refining documents inside the tools you already use, coordinating meetings end to end from scheduling to follow-up, managing tasks across tools like email, Asana, and Jira, and surfacing past decisions or context on demand. Go is aimed at professionals and teams who need real results rather than just a chatbot, and the Agent Store also lists agents for schoolwork, sales teams, and accountants. It is available through the Superhuman desktop app for Windows and Mac, browser extensions for Chrome and Edge, and mobile apps for iOS and Android, and it can also be used in your browser with nothing to install. Go is free to start and included in every Superhuman plan, while custom agent building is available on Pro, Business, and Enterprise plans. If you already use Grammarly, you can switch on 'Use Superhuman Go' in your Grammarly settings with no new install, and you can switch back anytime. Superhuman Go's primary value proposition is proximity: it brings proactive, context-aware AI to the apps and websites where work actually happens, backed by a team of customizable agents and Grammarly's writing intelligence, so better phrasing, faster replies, and well-prepared meetings arrive without you having to go looking for them.
Brainlist Calendar is a task and calendar app built from someone with ADHD, for people who want to get tasks and events done. It helps you plan your day with a list view that shows all events and tasks in one timeline, alongside a monthly calendar view that syncs automatically. The product's core promise is simple and stated plainly in its tagline: every notes entry shows up in your list AND your calendar. That means notes, tasks and events live in one place rather than being scattered across separate apps, and anyone who wants to finally get organized and stick with a single system can use it. It is designed with particular attention to people who struggle with normal calendars. The app grew out of a very specific frustration. The maker explains that she has ADHD, tried hundreds of calendars, and went back to her notes app every single time. Using a notes app simply worked better for her: as she puts it, her brain needs a list, not a sheet. But that approach had a cost — notes would get lost all the time, and she got sick of forgetting things, which is why she built Brainlist. A commenter on the launch pointed at the related flaw in most task apps: they assume you can categorize a thought the second you have it, and that is exactly the step that kills the habit. Brainlist is built around the opposite flow — capture now, triage later — so the moment of capture never depends on making decisions. The two primary surfaces of Brainlist Calendar are a list and a calendar. The list view shows all events and tasks in one timeline, so a task and an appointment from the same afternoon sit next to each other instead of in different tabs. The monthly calendar view syncs automatically, and the central behaviour of the product is that every notes entry appears in both places. In practice this means you write or dictate something once and it shows up as an item in your running list as well as an entry on your calendar, with no duplication and no manual copying between apps. For someone who has repeatedly abandoned conventional calendars, keeping the list as the working surface while the calendar stays in sync preserves a visual overview without forcing the spreadsheet-like grid that the maker says does not work for her brain. Brain Dump is the capture layer. With it you quickly capture a thought by typing it or dictating it, and then you can get back to it later without the note being buried away forever. This is the anti-forgetting mechanism at the heart of the app: rather than trusting memory or a note that sinks to the bottom of a long document, the captured item is preserved and flows into the rest of the system. The maker describes the capture as genuinely frictionless, and says the resulting pile goes into the Eisenhower matrix on its own. A user on the launch thread called the brain dump now, triage later flow the part that would actually get them to use the app daily, because it removes the categorization step from the moment of capture. Two features help turn a captured pile into a plan. The Eisenhower importance/urgency Matrix sorts items according to how important and how urgent they are, giving a structured view of where attention should go. The captured pile feeds into the matrix automatically, but the maker is explicit that triaging still happens manually: it takes two toggles and a tap-out per item to place something in a quadrant. She has said she is excited to improve this, and has asked users whether they would prefer a pop-up that lets them drag an item into a quadrant or an AI that decides the quadrant for them. The second feature is a spin-the-wheel picker for when you cannot decide what to start with — it picks a task for you, which short-circuits the hesitation that often stalls the start of the day's work. The Pro version is mainly about unlocking AI planning. According to the maker, Pro estimates event times, breaks tasks down for you, and preps reminders. If you record a meeting, Pro can break the recording down into tasks. If you travel somewhere, Pro also checks how long it takes to reach your event and reminds you when to leave, turning a static calendar entry into a travel-aware prompt. Pro additionally lets you track your actual houseplants with watering and feeding reminders, which shows how far the app stretches beyond conventional task lists into recurring real-world care routines. The maker has been open about what Pro includes and has invited users to suggest features they would want to see in the plan. Overall, Brainlist Calendar works by merging three things that are usually separate — notes, tasks and the calendar — and by splitting the act of capture from the act of organizing. You dump a thought, a task or an event into the app by typing or dictating, and it immediately exists in the list and on the synced monthly calendar. Later, when you are ready to think about priority, items move into the Eisenhower matrix, where you triage them by hand into importance and urgency. When you are ready to act, the spin-the-wheel picker can choose a task for you. The unique approach is this ordering: nothing requires a decision at the moment of capture, and no entry lives in only one view. The stated outcome for the maker is significant: she says the app genuinely changed her life, that she wanted to get organized and finally stick with something, and that she did. The design targets the two failure modes that made earlier attempts collapse — notes getting lost, and calendars that a list-shaped brain will not keep up with. A user who had been using the app for two days reported really liking it so far and called having notes, tasks and calendar all in one place pretty neat. The benefit is not a longer feature list; it is that one system holds everything, so there is nothing to migrate to and nothing that quietly disappears. Typical use cases follow the daily rhythm the app was designed for. Planning your day means opening the list view, which already contains both events and tasks in one timeline, and checking the synced monthly calendar for what is ahead. Capturing a thought on the go means either typing it or dictating it into Brain Dump so it can be dealt with later rather than being lost. When a long list makes starting hard, the spin-the-wheel picker chooses for you. When you are ready to sort, you triage the dumped pile inside the Eisenhower matrix. Pro adds use cases such as recording a meeting and having it broken into tasks, checking travel time to an event so you are reminded when to leave, and tracking houseplants with watering and feeding reminders. Brainlist Calendar is available as an iOS app, distributed through the Apple App Store. It is free to use, and a Pro subscription unlocks the AI planning features; subscriptions are available Weekly, Monthly or Yearly, each with a 7-day free trial. The product was launched on Product Hunt by maker Lara Hopf and lists Claude Code, Gemini 3.7 Flash and RevenueCat among the tools it was built with. Product Hunt topics for the launch are Task Management, Notes and Calendar. The maker is openly gathering feedback, including from users who struggle with normal calendars, and has said that a V2 is planned. Brainlist Calendar's value proposition is that it stops the split between notes and calendars. Every entry you capture shows up in your list and in your synced monthly calendar, the Eisenhower matrix and spin-the-wheel picker help you decide what matters and what to start, and Brain Dump makes sure nothing gets buried. For people whose brains work better with a list than a grid — and who have tried and abandoned plenty of calendars — that combination is the point.
GoodSocials is an AI social media manager for LinkedIn. It writes high quality LinkedIn posts from deep research or from your own data, you approve them on a board, and it schedules them so you post regularly — five times a week, close to daily. The example board on the site is set up for TimeTuna.com, a scheduling service, with the stated goal of inbound leads. Signing in with LinkedIn and pasting your website fills a board with seven posts in 90 seconds. GoodSocials says it is built for the person whose name is on the profile, and it is positioned around only authentic content: research, deep dives and your numbers, nothing else. The problem GoodSocials addresses is cadence and cost. A social media manager costs around $3,000 a month. Against that, GoodSocials notes that a manager posts most weeks, needs about a week of onboarding before first drafts arrive, and stops when you are on holiday — while the product posts every weekday, delivers first drafts in 90 seconds, and posts what you queued while you are away. The cadence section frames the issue bluntly: three posts in your last three months versus twenty posts next month, with the promise that profile views, followers and inbound leads follow a steady month. There is also an explicit quality argument running through the marketing, which is built around avoiding 'AI slop', cringe openings and clichés such as 'nobody talks about this' or 'it's not X, it's Y'. Every post GoodSocials writes is one of three kinds: market research, deep dive, or your numbers. Market research posts draw on comparisons of the market — for example, one card reports that of 12 scheduling tools compared that month, 9 put round-robin behind a paid tier and two moved it there last quarter. Deep dives go further into a single source: one card codes the most frequent week-one question from 1,200 support threads and notes it points somewhere the roadmap had not looked; another reviews Iyengar & Lepper (2000) and Scheibehenne et al. (2010) to explain why a product shows three slots, weighing one famous jam study against a meta-analysis of 50 experiments. Your numbers posts report figures read from your own connected tools, such as failed deploys per release falling about 62% after one CI rule on a small sample of 11 releases, or 31% of August signups choosing annual, up from 19% in July. The claim is that the content comes from research, deep dives or your numbers — nothing else. GoodSocials is also explicit about what it will not write. The site shows a rejected example: 'Nobody talks about this, but scheduling is broken. I spent 10 years in SaaS. Here is what I learned: It's not about the tool. It's about the mindset. Agree?' The stated rules are no cringe, no 'no one talks about this', and no 'it's not X, it's Y'. What it writes instead is grounded in a specific number with the source attached — for example a post reporting that reschedules fell from 18% in May to 7% in August after one change, with n = 9,412 meetings, an acknowledgement that seasonality could explain part of it, a holdout running in October, and a next question about no-shows. That stylistic discipline is the differentiator the site leads with. You stay in control through an approval board. Posts arrive as cards, you review and revise them, and only approved items are scheduled. Cards display the posting time, such as 'Publishes Tue 09:00', and the board reflects a weekday cadence of five posts a week. The pipeline shown on each card is: reads your tools, writes the post, draws the image, you approve, publishes. The cadence section states the product takes you from three posts in your last three months to twenty posts next month, and the aim is to show up every weekday because the rest — profile views, followers and inbound leads — follows a steady month. Correcting the output is a first-class feature, described as 'Correct it once. Every post after follows.' The mechanism has three steps. First, you leave a note on a card, such as 'Open on the number, not a question.' Second, that note rewrites the card and becomes a rule in your voice; rules accumulate under a VOICE heading, with examples like 'Reports, does not sell' and 'Opens on the number.' Third, every next post follows the accumulated rules. The site illustrates the compounding effect with a timeline: Week 1, one rule; Week 4, two rules; Week 12, five rules, every one from a note of yours. The Pro plan also lists three brand principles that learn from every revision. GoodSocials works from your own data by reading your tools. The example board lists connected data sources: PostHog, Stripe, GitHub, Plausible and Notion, all connected read only. Those sources feed the 'your numbers' post type, which is why a card can quote reschedule rates, signup mix or deploy frequency with the tool named underneath it. Images are generated as part of the workflow, since the pipeline includes 'draws the image', and the Pro plan includes up to 200 generated images a month. The site also notes what is coming next: Codex, Claude Code and Grok bot integrations. The overall approach is a repeating loop rather than a one-off generation. You sign in with LinkedIn and paste your website; the board fills with seven posts in 90 seconds, with 7 days free and then $100 a month. Each post then runs through the same five-step pipeline: it reads your tools, writes the post, draws the image, you approve, and it publishes at a scheduled time such as Tuesday 09:00. Research and deep dives come from outside sources with citations, while your numbers posts come from connected analytics. Because approval happens before publishing, and because corrections become persistent voice rules, the system is designed to improve week over week instead of resetting each time. The outcomes GoodSocials claims follow from the cadence: profile views, followers and inbound leads follow a steady month of showing up every weekday. The comparison table against a $3,000 a month manager lists the concrete differences — posts every weekday rather than most weeks, first drafts in 90 seconds rather than after a week of onboarding, and posting what you queued when you are on holiday rather than stopping. The board is full in 90 seconds, so there is no long ramp before the first post appears. And because every correction becomes a rule, the site argues the output moves closer to your voice over time, which is the basis for its 'only authentic content' positioning. GoodSocials is built, in its own words, for the person whose name is on the profile: founders, consultants, operators and agencies. The site maps each group to the kind of data it writes from — founders to Stripe numbers, consultants to Notion-based client work and engagement patterns, operators to GitHub and engineering numbers, and agencies to each client's own data. Pricing has three tiers with 7 days free on each and no setup fee. Pro is $100 a month for one LinkedIn profile, including three brand principles that learn from every revision and up to 200 generated images a month. Agency is $1,000 a month for up to 10 profiles with one board per client, where each client authorises their own profile so you never hold a password, and GoodSocials emails you before a client's access runs out. Consultancy is $2,000 a month, where Pasha, the founder, sets up your themes, principles and sources with you and holds a call with you every week. Plans can be changed or cancelled from the pricing page or settings. In short, GoodSocials is a LinkedIn posting system that combines deep research and your own product data with a human approval board and a voice that improves from every correction. Its value proposition is showing up every weekday with content drawn from real research or real numbers, at $100 a month instead of the $3,000 a month a social media manager costs.
Paragraph Notes is a privacy-focused Markdown notes app for Mac, created as a private place to write and keep notes in plain text. The app is built around a simple, distraction-free plain text editor that supports Markdown formatting, so users can structure their writing with Markdown while their notes stay as plain text. It is intended for Mac users who want a straightforward environment for writing notes rather than a large, feature-heavy system. Its stated purpose is to let people write notes and keep them on their own machine: Paragraph Notes collects no data, never connects to the internet, and stores notes locally in an open, portable format. Many modern note-taking tools run in the cloud, require an account, move notes between servers, and gather information about how the app is used. For people who would prefer their writing to stay on their own device, that model creates friction: the notes live somewhere else, and the tool's behaviour depends on a network connection and on a company's policies. Paragraph Notes takes a different position. It is described as privacy-focused, it collects no data, it never connects to the internet, and notes are stored locally in an open, portable format. Without network access and without data collection, the writing happens entirely on the Mac, so the notes are not sent anywhere and no usage information is gathered. At the centre of the app is its Markdown editor. The website describes the editor as plain text with Markdown formatting, which means writing happens in a plain text field where Markdown is used to add formatting as you write. This keeps the writing surface simple and predictable, and it keeps notes tied to a widely understood, text-based syntax rather than to a proprietary rich-text format. For anyone already comfortable with Markdown, this is a familiar way to write; for anyone new to it, the app provides a user guide to help them get started. The combination of plain text and Markdown is what makes the notes practical to read, edit, and move around. The interface is described as a distraction-free writing environment. That means the app is designed to keep attention on the text rather than on toolbars, panels, or surrounding chrome. A distraction-free editor matters because note-taking is often a small, frequent act: a thought to capture, a paragraph to draft, a list to jot down. Reducing the interface to the essentials makes it faster to start writing and easier to stay with what you are writing. Because the app never connects to the internet, the writing environment is also independent of a network connection, so starting a note does not depend on being online. Privacy is the headline characteristic of Paragraph Notes. The app collects no data and never connects to the internet, so notes are not transmitted to a server and no usage information is gathered. Notes are stored locally in an open, portable format, which means they live on the Mac rather than in a company's cloud, and they are kept in a form that is not tied to the app itself. For users who think about where their writing lives, that combination is the point: private by default, offline by design, and stored in a format that stays useful outside the app. The website's one-line summary captures the intent exactly: Paragraph Notes is a private Markdown notes app for Mac. Overall, Paragraph Notes follows a simple model. You install the Mac app, open it, and write notes in plain text with Markdown formatting in a distraction-free editor. Notes are written and kept locally on your Mac in an open, portable format; the app collects no data and never connects to the internet. The product is distributed through the Mac App Store, where it is listed as Paragraph Notes, and a user guide is available on the website for getting started. The business model is equally simple: the app is free to use for up to 20 notes, and a Pro upgrade is available through a one-time in-app purchase for unlimited notes, priced at $19.99. The benefits follow directly from those choices. Writing in plain text with Markdown keeps notes simple and readable, with structure added through syntax rather than through heavy formatting controls. Local-only storage keeps notes on the user's own Mac and removes dependence on a network connection or on a third-party service staying online. Because no data is collected and the app never connects to the internet, users do not have to weigh up what information about their writing might be gathered. And the notes themselves, stored in an open, portable format, are not trapped inside the app. For people whose main concern is owning their own notes, these are the outcomes that matter most. In practice, Paragraph Notes fits workflows built around writing on a Mac. A user might keep a personal notebook of notes, drafted in plain text and formatted with Markdown, on the machine rather than in the cloud. Someone who already writes in Markdown can use the app as a quiet place to draft, keeping the same plain text conventions they use elsewhere. Because notes are stored locally in an open, portable format, they can be kept, backed up, and read outside the app rather than being confined to it. Writers who want minimal tooling can use the distraction-free editor to remove everything but the text, and anyone who starts with the free tier can keep up to 20 notes before deciding whether to upgrade. The user guide on the website provides starting instructions for these workflows. Paragraph Notes is aimed at Mac users who want a private, simple notes app rather than a cloud-connected service. It suits people who care about where their notes are stored, who prefer plain text and Markdown over proprietary formatting, and who want a distraction-free place to write. The app is distributed through the Mac App Store, and the website links directly to its App Store listing. Pricing is straightforward: Paragraph Notes is free for up to 20 notes, and Pro is a one-time $19.99 in-app purchase that removes the note limit, giving unlimited notes. The website and the user guide are the primary sources of documentation, and the app itself is presented in English. Paragraph Notes positions itself on a single, clear promise: a private Markdown notes app for Mac. It combines a plain text Markdown editor, a distraction-free writing environment, and local-only storage in an open, portable format with a policy of collecting no data and never connecting to the internet. Free use covers up to 20 notes, and a one-time $19.99 Pro purchase unlocks unlimited notes. For Mac users who want their notes to stay on their own machine, written in plain text and formatted with Markdown, Paragraph Notes offers exactly that and little else.
ShroomPen is a browser writing assistant that opens right where you are writing, inside the page you are already working in. It can use the page you are on, or other pages you pick, so you never have to explain everything again in a separate AI chat. It performs four actions on the text in front of you — Reply, Rewrite, Fix Grammar, and Translate — and it is aimed at day-job writing rather than personal prose: a support reply, a billing answer, a quote, or a form's long answer, where the facts matter more than the prose. ShroomPen ships as a Chrome extension that you open with Ctrl+Q, and it works on any normal website, such as your inbox, a CMS, a support tool, or a doc in the browser. The problem ShroomPen targets is that everyday writing help usually lives somewhere else. You leave the page, open a separate AI chat, and paste in context that is already open in other tabs, or you re-explain everything from scratch. ShroomPen stays on the page instead, and lets you point it at the exact pages and tabs that hold the details. The second half of the problem is privacy. Work writing often carries customer names, order numbers, delivery dates, billing details, and proposal facts, and sending that material to a cloud service is not always acceptable. ShroomPen is described as a privacy-first browser writing assistant that lets you instantly reply, rewrite, fix grammar, or translate text across any website without sending sensitive data to the cloud. It is designed to balance AI convenience with data privacy by keeping all your page, field, tab, and session context securely on your device, giving you complete control over your everyday web workflow. Whatever is in the field is what ShroomPen works on. You pick an action and can see the before and the after. Reply answers the message in front of you, and your rough note is the brief. Rewrite produces clearer and more natural text with the same meaning. Fix Grammar corrects spelling and punctuation in the language you wrote. Translate lets you pick the language, and only the text you chose is replaced. The site demonstrates this with a live chat example: an agent types a rough note that says the order looks delayed and might arrive tomorrow, and after pressing Reply the field holds a finished answer that opens with a greeting, cites order #4821, states that UPS has it in transit, and gives the updated delivery date. The customer's name, order number, and delivery date are already filled in, so the agent never has to go looking through tabs for them. ShroomPen builds its draft from the sources you tick. It can use the current page, selected tabs, the field itself, selected text, and session recents. As the site puts it, invoices, docs, notes, and a CRM page can be ticked when they hold the details, and the text is built from them; untick one and it loses what only that tab knew. A proposal example shows this in practice: while drafting in a document at docs.example.com, the assistant can draw on a customer notes page from a CRM and a feature page from a product site, and the resulting paragraph talks about a shared inbox, saved replies, and ticket history, and notes that this matches the customer notes. Because the context is chosen rather than scraped wholesale, you decide exactly which pages contribute facts to the draft, and which stay out of it. That is what makes the output specific instead of vague. Privacy and control are treated as first-class features. Provider settings stay in Chrome extension local storage, and generation requests go directly from the extension to the endpoint you configure. In v0 there is no ShroomPen backend: no required ShroomPen account, no hosted model, no extension telemetry, no ads, and no ShroomPen server path. Source control is explicit, meaning current page and tab text are read only when those sources are enabled. ShroomPen also stays out of password fields and other sensitive inputs, with disabled domains and sensitive-field exclusions keeping risky surfaces out of the flow. The first time you open it, ShroomPen tells you what it can read and waits for you to agree; after that it only opens when you ask, and you choose which page or tab it uses each time. You can open the request before it goes out and read exactly what your provider will receive, with your key hidden, and edits made there apply to that one request. If you want ShroomPen to leave a site alone entirely, you add that site to the off list in settings and it will not open there at all. The overall workflow is deliberately short. First you install the Chrome extension. Then you connect a provider: sign in with OpenRouter, or add an OpenAI, Anthropic, Gemini, or OpenAI-compatible key. No ShroomPen account or hosted model is required. Finally you write — open ShroomPen with Ctrl+Q and attach only the context you want. If you prefer to run a model on your own machine, you can, as long as it speaks the OpenAI API: point ShroomPen at your local server the way you would any other provider, and no key is needed. The result always comes back in ShroomPen first, so nothing is changed automatically. You decide whether to copy the text, drop it in where your cursor is, or replace the text you had selected. The benefits follow from that design. Because the draft is assembled from the pages you attach, the facts travel with the text: names, order numbers, dates, and product details arrive in the right place without a search through open tabs. That means fewer vague statements and less time spent hunting for past replies and context, and it produces faster, more consistent answers for the people receiving them. Your rough note is enough of a brief to produce a finished, on-message reply. Just as importantly, you stay in the page you were working in, so there is no tab-switching to a separate chat and no re-explaining of background. Since context is limited to what you enable, and the request goes straight from the extension to your chosen provider, you keep a clear view of what leaves your browser and what does not. The use cases described are practical ones. A support agent handling a live chat can press Reply and get an answer that already contains the customer's name, the order number, and the delivery date pulled from the tabs holding that information. Someone drafting a proposal in a browser document can attach a CRM notes tab and a product feature page and get a paragraph that matches the customer's stated pain points rather than generic claims. A billing answer, a quote, or the long answer in a form can be produced the same way, with the underlying facts fixed in place. Fix Grammar is for polishing text you already wrote in your own language, and Translate replaces only the text you selected, in the language you pick. The site frames all of this as writing where the facts have to be right, as opposed to birthday notes, toasts, or a cover letter's opening, which it says you should write yourself. ShroomPen is therefore for people whose work writing has to sound like them and also has to be correct, on sites they already use. Integration is through the browser: it opens on any normal website, including inboxes, CMS tools, support tools, and browser documents, and stays off pages the browser keeps to itself, such as settings and the extension store. On the provider side it connects to OpenRouter, OpenAI, Anthropic, Gemini, or any OpenAI-compatible endpoint, including a local server that speaks the OpenAI API. Pricing as listed is a Live Free plan at $0, covering page, field, selected text, selected tabs, and session recents context, the Provider Request Preview before or after generation, and no required ShroomPen account, extension telemetry, ads, application backend, or hosted model. The takeaway is that ShroomPen brings AI writing to the place the writing actually happens, with the facts you select attached and nothing else. It replies, rewrites, fixes grammar, and translates on the page, keeps the context on your device, and routes requests only to the provider you configure. For anyone who writes support replies, billing answers, quotes, or proposals where accuracy matters, it is a way to get a finished draft without leaving the page and without handing over more information than you chose.
ToneBird is a socially fluent AI reply assistant for Mac and Windows that helps you answer messages without getting stuck on words. It understands your relationships, what you have already said, and where you left off, then drafts the next reply in your voice directly inside Gmail, Slack, WhatsApp, iMessage, Discord, WeChat, Lark, Google Chat, X, Reddit, LinkedIn, Instagram and other apps you already type in. Instead of leaving the conversation, building a giant prompt, explaining the backstory and bringing the output back, ToneBird works beside the reply box. You open a reply, choose the wording that fits, tweak it if needed, and insert it into the conversation before reviewing and sending it yourself. Replying well sounds simple until context is involved. A client asks for the price you agreed on weeks ago, your manager asks for an update on something that already slipped once, or a customer pushes back on a renewal increase you have discussed before. A good answer depends on the relationship, the history and the commitments, and the apps people type in rarely keep that context available at the moment of writing. Generic AI assistants can produce fluent text, but they start from a blank page, so the person writing has to re-explain who they are talking to and what happened previously. ToneBird is built on the idea that the right reply starts with the context, and that the context already lives in your conversations and your files. Relationship memory is the core of the product. ToneBird uses your earlier conversations when drafting a reply, including the date you promised to confirm or the request a person made after a deadline slipped. It also pulls in facts from your files: when a client asks for the agreed price, ToneBird can find it in your connected proposal and include it in the draft. The interface shows a "Knowledge Recalled" panel, so you can see what the assistant remembered for that specific reply, such as a checkpoint that was requested, a scope change from last quarter, or a previously reported issue. You retain control over what is kept: facts and writing preferences learned from the conversation are surfaced for review, and you choose what to save. ToneBird also adapts wording to the person you are replying to. A client waiting on a deadline needs a different reply from a teammate checking in, and the assistant uses your past conversations with each person to adjust the phrasing. For any message, ToneBird can produce several options so you can compare three replies, then tweak the one you want before inserting it. Tweak controls include More Precise, Warmer and Add Humor, and there are context switches for Internal or external replies plus sliders for Directness, Formality and Warmth. Saved preferences such as "keep replies concise" carry into later conversations, so you skip the introduction and start from your own style. ToneBird is designed to follow you across apps and languages rather than pulling you into another tool. It works beside readable reply fields in apps such as Gmail, Slack, WhatsApp, iMessage, Discord, WeChat, Lark and X. When a message arrives in another language, ToneBird can draft the reply and show a translation, as in the example where a customer writes in Spanish and the assistant produces a Spanish answer with an English translation underneath. Beyond replying, ToneBird turns the conversation into follow-up work: when you and another person agree on a time, it prepares the event in Apple Calendar or Google Calendar from your chat, and a follow-up list gathers promised quotes, unanswered questions and dates to confirm in one place, marked by the app they came from. ToneBird also lists English, Español, 中文 and 日本語 among its available languages. The overall workflow is deliberately short. You click the orb, or press Option twice on Mac or Ctrl twice on Windows, while you are in a reply field. ToneBird reads the thread, recalls relevant context from your relationships and files, and shows suggestions; you click the arrows or use the left and right arrow keys to move between replies, tweak the wording, and then insert it into the reply box. Inserting is not sending. ToneBird does not send messages for you: the draft goes into the reply box, and you review it and use the app's own send button, so the final decision always stays with you. The benefits follow from that approach. You do not have to be stuck on words again, and you do not have to postpone a reply because you cannot face reconstructing the context. Promises, prices and dates that would otherwise require digging through old threads or documents are brought forward automatically, which reduces the risk of contradicting yourself or repeating an issue that was already raised. Because the assistant keeps your voice intact rather than making every reply sound generically "better", the person on the other side of the conversation experiences a consistent version of you, whether they are a customer, a manager or a teammate. Follow-ups that would normally be forgotten become visible tasks, and scheduling happens from the conversation itself instead of in a separate calendar app. Concrete scenarios show how this plays out. In Slack, a manager asks for a launch plan by Friday after a previous slip; ToneBird drafts a reply that commits to a core plan by Friday with a checkpoint later that day. In WhatsApp, a customer says a renewal increase is hard to approve; ToneBird recalls that the scope changed last quarter and drafts a reply that offers to work through the renewal over coffee near the customer's office, with proposed times. In Gmail, a customer writes in Spanish about a repeated export problem; ToneBird drafts a Spanish reply apologising and committing to check workspace permissions and send findings before 4 PM, with an English translation. When the customer later confirms Thursday at 4, ToneBird prepares a 30-minute proposal review event that can be added to Apple Calendar or Google Calendar, and the resulting task list reminds you to send a revised quote or confirm a delivery date. ToneBird is for people whose work happens in email and chat apps, including customer-facing and sales conversations, client renewals, manager and teammate updates, and anyone who needs to reply across several apps and sometimes several languages in the same day. It runs as a downloadable app for Mac and Windows, chosen on the download page, and it works beside readable reply fields in supported apps, though support depends on the app and some fields may not be readable. Your tone profile, person cards, learned corrections and usage counts live on your device, and you can export or delete them any time in Settings. On data handling, the model provider receives the context and instructions needed for your reply: with a managed model ToneBird forwards the request, and with your own model key writing requests go directly to that provider. Pricing starts free, and scales through Standard at $9.90 per month with 100 uses every day and 100 contacts, Pro at $39.90 per month with 1,000 uses every day and 1,000 contacts, and Enterprise with custom pricing for your team; billing is monthly or annual, with about 20 percent saved annually. ToneBird's primary value is that it turns the context you already have into the reply you need next. It remembers your relationships, retrieves relevant facts from past conversations and connected documents, adapts the wording to the person and keeps your voice intact, all inside the apps where the conversation is already happening. You pick a suggestion, tweak it, insert it, and send it yourself. Free to start and with room to grow, ToneBird is aimed at anyone who wants to stop being stuck on words without handing over control of what gets sent.
Edyt is a lightweight desktop app for Mac and Windows that adds AI actions to any text field. You select text, press a shortcut, and the result appears in place, right where your cursor is. It is aimed at anyone who writes across many different programs each day, such as email clients, browsers, notes apps, and chat apps. The available actions are Rewrite, Proofread, Alternatives, Explain, Translate, and Use Prompt, and the product positions itself as AI that lives right where you write. A free plan is described as enough for most people, while Pro removes the usage limits. Writing rarely stays inside one application. A reply is drafted in email, a paragraph is tightened in a document, a question is answered in a chat window, and a contract arrives as a PDF. Conventional AI tools and grammar checkers require copying text out, switching windows, and pasting results back. Edyt is built around avoiding all of that: the site describes selecting text in any app with no copying and no switching windows. The developers also argue their approach beats a traditional proofreader in specific ways, saying Edyt does better at explaining text, sounding like you, offering a genuinely free tier, working in every app on a Mac, and staying out of your way. There is a further gap the product targets: text you cannot even select, such as a PDF, a screenshot, or a shared screen. Rewrite polishes tone, grammar, and clarity in place, and the listing adds that Rewrite keeps your tone and follows your instructions. Proofread is built for catching mistakes thoroughly. Select the text, press Control+Shift+H, and every issue is flagged in place and grouped by kind on the left. The down arrow moves between issues and Enter fixes one; you can also fix them all at once. The text stays editable, so you can type and then re-check to catch anything new, and Insert pastes the result back in, formatted, right where it came from. The demo shows a correctness control, an ignore option, and an easy-to-read word count as the changes are reviewed. Alternatives answers the question of how else to phrase something. Select the text, press Control+Shift+L, and Edyt suggests options, listing several ways to say the same thing. If none of them fit, Show another produces a genuinely new one, never a repeat. You move between options with the down arrow and press Enter to open Insert or Copy, so a suggestion can go straight into the app or onto the clipboard. Explain takes the opposite direction and focuses on understanding. Press Control+Shift+X and definitions appear on the left with the explanation on the right. You can ask a follow-up question, which keeps the conversation going, so an explanation can deepen as far as you need. The site sums both up with the lines See it several ways and Understand instantly. Translate handles messages written in other languages. Select the message, press Control+Shift+T, and the meaning is rendered in your own language; select it again with the same shortcut and choose their language to reply. Insert drops the translation into the reply box and you send it. Recent languages are shown for quick switching, and you can ask how to adjust a translation if the wording is not quite right, which is useful for client conversations where precision matters. Use Prompt runs your own saved prompts on anything you select. Press Control+Shift+J and Edyt runs your last prompt right away, or you pick another prompt from the list on the left. Personalized prompts remember how you like things phrased, you can ask follow-up questions to refine the result, and Insert drops it straight into the document with no retyping. The listing adds that saved prompts follow your account to every machine. Not all text can be selected. Edyt's screen reading mode covers that case: hold Option on Mac or Alt on Windows and drag over a PDF, a screenshot, or a shared screen. Edyt reads the text on your device, using Apple's built-in recognition on Mac and the built-in Windows OCR on Windows, so the image never leaves your device. On Mac this uses the Screen Recording permission, only to capture the area you drag; Windows asks for no extra permission. Once the text is read, the same actions apply, so you can explain, proofread, translate, or run a prompt. In one demo a lease agreement PDF is dragged over and an automatic renewal clause is explained in plain language, including the 60 days' written notice requirement, followed by a direct question about the deadline. Edyt's workflow is described in three steps. First you select your text by highlighting anything in any app, such as email, docs, or chats, without copying or switching windows. Second, you press Control+Shift+Space to bring up the toolbar and pick the action you want. Third, Edyt calls the AI and drops the result right where your cursor is, described as instant and in place. Each action also has its own shortcut for speed: Control+Shift+C for Rewrite, Control+Shift+H for Proofread, Control+Shift+L for Alternatives, Control+Shift+X for Explain, Control+Shift+T for Translate, and Control+Shift+J for Use Prompt. Every action's shortcut can be remapped, and you can write your own prompts and organize them in your library from your dashboard. The stated benefits follow from that design. Results land where the cursor is, so there is nothing to copy and no window to switch to. Rewrite keeps your tone while it cleans up grammar and clarity, which matters when the writing has to still sound like you. Alternatives produces phrasing better than what you typed without you having to hunt for it yourself. Explain and Translate go as deep as you ask, because each result can be followed up with another question. Proofread can fix every issue at once or one at a time, in text that remains editable. Use Prompt turns your own preferences into reusable instructions, and saved prompts follow your account to every machine you use. The demos on the site map onto everyday tasks. In a chat, someone asks a colleague to look over an email to a client because the grammar is a mess; Edyt fixes it in seconds. Another demo proofreads an essay saved as Reflections.docx, flagging repeated words and awkward phrasing in place while the writer keeps editing. A third shows a client email being reworded several ways before it is sent. In a messaging app, a Spanish message about billing terms and an auto-renewal clause is translated so it can be answered in English. A lease agreement PDF is dragged over to explain an automatic renewal clause and its notice deadline. And in a document editor, a saved prompt rewrites a weekly sync announcement into tighter, more focused wording. Edyt is for people who write in many apps rather than one: email, documents, chats, and browsers, on Mac and Windows. On Mac it runs on macOS 12.3 (Monterey) or later, on both Apple Silicon and Intel. On Windows it runs on Windows 10 or later, 64-bit, currently as a beta, and the Product Hunt listing notes Ubuntu is next. It works inside any app with an editable text field. The free plan includes generous daily, weekly, and monthly usage with no credit card required, and Pro removes the limits for unlimited use. Edyt is not on the Mac App Store because reading and typing text in other apps needs macOS's Accessibility API, which does not work inside the App Store sandbox, so it is distributed directly from the site, signed and notarized by Apple. Privacy is stated plainly. Your text is sent over an encrypted connection for processing and then discarded; Edyt says it never stores it, logs it, or uses it to train AI models, with details on a Security page. The screen reading feature is handled on the device itself, so images never leave it. The footer repeats the same commitments: signed on Mac and Windows, your text is never stored, and never used to train AI models. Edyt's proposition is narrow and clear: AI for text, wherever it happens to live. By putting Rewrite, Proofread, Alternatives, Explain, Translate, and your own saved prompts behind a single shortcut inside any app, and by adding on-device reading for text that cannot be selected at all, it removes the copying, window switching, and friction that usually sit between you and a finished sentence.
thestory.run is a web app that coaches employees to write their own LinkedIn posts. Its stated purpose is to be a writing coach for corporate influencers — the people whose job quietly includes LinkedIn, from founders and salespeople to recruiters, engineers and experts. The coach asks questions, helps a person find the story inside a rough thought, and hands them a structure to write into. It never writes the post, and it never rewrites your words. Most AI tools take over the writing; thestory.run flips that relationship so the person stays the writer and the tool stays the coach. The product opens with a blunt observation: "We stopped writing." Three problems are named. First, AI slop is real — the site cites that 81% of long-form posts on LinkedIn are likely AI-written, and argues the personality is gone even when the AI "has your voice." Second, people are too busy to think: it is not that you have nothing to say, it is that nobody asked you the right question about it. Third, people use AI wrong — let it write for you and you slowly lose your own voice and the ability to put a thought into words. The result, as the site puts it, is that writing goes from something you liked to one more task to get over with. thestory.run's stated belief is that AI is here to support humans, not replace them: leave the edits to AI, and you be the superstar. The core experience is one real session that runs from a messy thought to a finished post, in three steps: share a rough thought, talk it through, write it yourself. You drop in the raw thoughts and the coach helps you uncover the story behind them, making the point you do not see yet. It asks one question at a time — in the example shown, it answers an observation with "That's an interesting observation, I bet you've watched it play out somewhere specific. What did that look like?" — and then picks up on the honest line the writer had not said out loud. Once your thoughts are out, the coach lays out the arc that helps you tell the story. The example arc has three beats: open on the moment, name what's really going on, and the pivot. Then you write it, and the coach turns editor: it marks what carries the post and what to try. The marks shown include "Keep · your spine" (the whole post in six words, let it stand) and "Move · your opener" (sharper than the meeting scene, open on it). The coach can move your opener to the top reversibly, with an undo, and the framing the product uses is 100% you, 0% coach — the words on the page are the writer's. Between the posts, the coach keeps the week moving so you never have to guess what to do next. A weekly to-do shows the plan at a glance, such as one post on leadership and shaping a saved team culture idea. Daily reflections offer a nudge from your coach — for example, "What have you changed your mind about in remote work?" — with an answer link. A posting streak and activity calendar track momentum (the example shows five weeks in a row, with the message "Showing up is the whole game"), and achievement stickers such as "posted on every topic" keep you accountable without guilt. A "finish drafts while they're hot" area surfaces drafts with labels like leadership/fresh/nearly there, or culture/a few lines in, and asks whether you are ready to write this one. For teams, thestory.run treats corporate influencing as a team sport. Most people start alone, and bring the team when they are ready. A team sees who is posting, on which theme, and what is still uncovered — but drafts, ideas and coach conversations stay private to the writer, not their teammates, not their admin, not their boss. Three team features are described. Campaigns: an admin sets a theme and a window, such as a hiring push, a launch or a conference, and the coach folds it into everyone's week as something to draw on; nobody gets handed a post to write. The team calendar shows who is posting, on what, and which days nobody has taken, so the team spreads out instead of everyone publishing on Tuesday. Wins: people share what landed, if and when they want to, and colleagues cheer, because watching a colleague's post open a real door is what gets the next person writing. The unique approach is what the product calls the writing tool that refuses to write. The coach asks one question at a time, underlines the strong lines you already wrote, and offers a structure to write into rather than a draft to approve. Every word you publish is a word you wrote. There is exactly one exception, called cheat-mode, and you have to reach for it on purpose: activated on a piece you have already started, the coach writes the post from what you brought — your rough thought, your conversation, the lines you kept, the arc you agreed on, and a few of your own published posts so it can hear how you write. Cheat-mode spends one of a small monthly allowance: one cheat for every four posts in your plan, never more than three a month. The post is marked permanently as coach-written and does not count toward your progress. Publishing is also opt-in: the product posts to LinkedIn only if you ask it to, on a post you have already written and read, and it never publishes anything you have not pressed the button on; you can also publish by hand and simply tell the app it happened. The benefit the product promises is that what you publish sounds like you, because you wrote it. Testers quoted on the site describe writing with the coach as feeling like therapy, like writing in a notebook that helps you think better, and as a conversation style that moves one step at a time. One tester's complaint about writing with AI today — "90% of what AI writes, I rewrite it, because it's not how I would write" — is contrasted with a founder's reaction to her first coached draft: "Wow, I kinda wrote it in one breath. I didn't know I can do that." Other quotes say the tool makes people want to write again, helps them find the substance instead of "some blah blah," and offers guidance for people who feel they do not have the time or capacity to think freely. Concrete workflows described in the content include turning a messy observation into a finished post in one coaching session; keeping a weekly posting rhythm with a plan, daily reflections and a streak; rescuing drafts that are nearly there or only a few lines in; coordinating a team around a campaign window such as a hiring push, launch or conference so several people write on the same theme without anyone being handed words; and spreading posts across a week with the team calendar so days like Thursday are not left open. thestory.run is aimed at people who post under their own name at work — founders, salespeople, recruiters, engineers and experts — and at teams that want corporate influencing to work company-wide. It is free forever for teams of five people or fewer, with everything included: people, channels and every feature, no credit card, no trial clock, no feature gates. From the sixth person it becomes pay as you go at 1 euro per coaching session plus tax, and nothing else; prices are in euros, and checkout in over 150 countries lets you pay in your own currency, with Stripe doing the conversion at its own rate and a small currency fee. It is built by Swat.io GmbH in Vienna, Austria, operated under the GDPR and the Austrian Data Protection Act, and runs in the European Union, with the privacy policy and data processing agreement published on the site; privacy is enforced in the data layer rather than hidden in the interface. Swat.io is a social media management tool more than 18,000 people use, built by fifty people over fifteen years with no investors and behind social media for 1,700+ brands; it provides the content calendar, community management and insights, while thestory.run is the other half — the coach that helps a person find and write the post in the first place. The takeaway: thestory.run is built for teams whose people already have things to say but do not know how to say them. Instead of generating posts and leaving a human to edit the machine, it coaches each person to write in their own voice, keeps the week moving between posts, and lets teams coordinate themes without handing anyone words.
Jevtown is a social network where people write and 10,000 AI personas read. You post a text, a listing, a product or a headline, and within seconds the town reacts: most scroll past, some like, repost, block, write to the seller or buy. The purpose of the product is to let a writer see how an audience would respond before publishing anywhere that actually matters. Every reader is a computed persona with a name, an age, a job and a city, and the English-speaking town is described as having 10,002 residents. Jevtown is aimed at anyone who writes for an audience — someone drafting a social post, a seller writing a listing, a maker describing a product or a writer testing a headline — and it requires no sign-in to use. The problem Jevtown addresses is the uncertainty that comes before publishing. Once a post, a listing or a headline is out in the world, the reaction is already public: readers scroll past, or they stop, or they block, or they buy, and there is no way to take that back. Jevtown moves that moment of truth earlier. Instead of guessing how a piece of writing will land, you can watch a simulated town react to it in seconds and then decide whether the text is worth publishing at all. The product puts it plainly: a weak text dies for half a cent and a good one reaches everyone in 14 seconds. The same logic applies to a marketplace listing, where the difference between two ways of writing the same item can decide whether buyers write to the seller or smell a scam. Jevtown accepts four kinds of input, each selectable from a single composer: Post, Listing, Product and Headline. The composer allows up to 2,000 characters, shown as a live counter labelled 0 / 2000. When you submit, the English-speaking town reads it — 10,002 residents — and you will see who likes, reposts or blocks it. The site also demonstrates a Ukrainian town: the homepage opens with a saved example labelled "A saved example · the Ukrainian residents" alongside a 01 / 02 selector, and residents in the sample feed carry Ukrainian names and cities such as Dnipro and Zhytomyr. Each resident is identified by a first name, an age, an occupation and a city — for instance Zoe, 20, a student in New York, or Yurii, 56, a nurse in Zhytomyr — and their individual reactions are listed next to their profile. The core mechanic is a wave system that decides how far a piece of writing travels. A post first goes to the 600 residents it should matter to, and it reaches the next 1,500 only if more readers were glad than annoyed. From there it can keep climbing: the feed shows posts that were seen 600 times, 2,100 times, 5,100 times and up to 10,001 or 10,002 times. A listing in the saved example went into a second wave and reached 2,100 personas after readers responded well to it. This staged distribution means that a weak text stops early while a good one reaches everyone, and the counts of how many people saw, stopped, were glad, were sorry, reposted, clicked or bought are reported for each post. Every stage is measured in a small, consistent vocabulary of reactions. Readers can scroll past, stop, be glad, be sorry, repost the post, click a headline, write to the seller, smell a scam or buy the product. The saved iPhone listing shows the pattern: 2,100 saw it, 142 wrote to the seller and 6 smelled a scam, with a Replay control and a visibility setting offering "To the public feed" or "By link only". Because the residents are computed personas rather than anonymous visitors, Jevtown can break their behaviour down by interest, job, age, city and budget, and tell you who stopped, liked, reposted or blocked your text. In the sample feed you can see individual reactions attributed to named residents, such as Iryna, 37, a copywriter in Dnipro who bought it. Jevtown's unique approach is to make the audience itself the product. Beyond feed reactions, a listing or product can be tested with a price ladder: prices can be named in ₴, $, € or £, and every resident who stops at the product is asked for the highest of those prices they would pay. The result shows how many buyers each price gets and which one earns the most — described as a demand curve over a product's price ladder. For listings, Jevtown also reports the questions buyers would ask first. The whole experience runs on the web without sign-in, costs as little as half a cent for a text that dies early, and can show a finished result in 14 seconds. The public feed itself can be sorted by Latest or Travelled furthest, and at the time of writing displayed 148 texts seen 260,005 times. The benefit for a user is a decision made before publication instead of after. Instead of publishing and waiting, you get an immediate reading of whether the text connects and who it connects with: the people who stopped, the people who were glad, the people who reposted and the people who blocked. Sellers learn the questions buyers would ask first, which can be answered inside the listing itself, and they get a view of which price point earns the most. Headlines can be compared by how many people stopped and clicked versus how many were sorry. Because each experiment is cheap and fast, a writer can afford to test several versions of the same text and keep the one the town responds to. The clearest use case shown on the site is a marketplace listing. The same iPhone 13 listing is written two ways — one version offering full details and payment on inspection, the other demanding advance payment only — and the site shows how the town reacts to each, including the number of personas who wrote to the seller and the number who smelled a scam. Another use case is the headline: the feed shows headlines such as "New research solves cancer" that were seen 600 times, with hundreds of residents stopping and dozens clicking. Products can be tested with the price ladder to compare demand at different prices, and general posts — opinions, jokes, technical write-ups, ideas — can be run through the town to see whether the audience stops, is glad, reposts or simply scrolls past. Jevtown is for people who write for an audience and want a fast read on it before going public: sellers writing marketplace listings, marketers writing headlines, makers writing product descriptions and anyone posting to a social feed. The product runs in the browser, requires no sign-in and costs as little as half a cent for a text that dies early. The sample content on the site covers both an English-speaking town of 10,002 residents and a Ukrainian town, and prices for the price ladder can be expressed in hryvnia, dollars, euros or pounds. The site mentions no third-party integrations and does not describe a technical stack, so the experience appears to be entirely self-contained. Jevtown is a sandbox for writing in public without the consequences: a town of 10,000 computed residents who read your post, listing, product or headline in seconds and tell you — by scrolling past, stopping, liking, reposting, blocking, writing to the seller or buying — whether the text is worth publishing. A weak text dies for half a cent; a good one reaches everyone in 14 seconds.
slop-grader is a rule-based command line tool that evaluates documents against custom rulesets and produces document scores and line-by-line flags to guide auto-fixing with an AI agent. It is built for anyone who writes, edits, or reviews text and wants that work checked against an explicit, repeatable standard rather than a vague impression. The tool is open source, runs locally in a terminal, and is designed around the idea that you curate a ruleset for your own use case and then reuse it as a powerful way to grade text consistently. Rather than rewriting a document for you, slop-grader grades it, tells you which lines failed which rules, and hands off instructions that an AI agent can act on to produce sharper copy. The context behind slop-grader is the flood of AI-generated writing. Text produced quickly by large language models tends to be padded with filler, stacked with buzzwords, and vague about what it actually promises. Reviewing that text by hand is slow, subjective, and inconsistent: two readers can disagree about whether the same paragraph is clear, and nobody notices a slow drift in tone across a long document until a reader finally complains. slop-grader takes a different position by making each check explicit. Instead of asking whether a document reads well, you express what you care about as a rule, and the tool answers that rule for every line it reviews. The result is a score plus a list of specific lines that need attention, which makes the standard applied to a document visible and repeatable. The heart of the product is the ruleset. Rules are written in plain language and can be anything you are able to phrase as a question. Some rules are evaluated line by line, such as "Does this line make a promise that requires a legal disclaimer?" Others are evaluated across the entire document, such as "Does the opening earn the reader's next 30 seconds?" That flexibility means the same tool can cover very different jobs: SEO checks on a page, legal clause review, tone of address, or keeping a formal or informal register consistent. A concrete example given by the maker is German address: keeping "Du" versus "Sie" consistent throughout a document. Because rules are yours to define, words that are buzzwords in one industry but completely normal in another are handled by simply writing the rule that fits your use case. slop-grader also ships with built-in rules, so you do not have to start from nothing. The included checks cover English grammar, German grammar, and AI filler detection. The maker notes that once you have built a curated ruleset for your use case, the tool becomes very powerful, and there is a built-in skill in the repository for creating custom rules, published as a SKILL markdown file on GitHub. That skill is intended to help you author the plain-language rules your workflow needs, whether they concern promises and disclaimers, openings that hold attention, SEO conditions, legal clauses, or tone of address. The output format is deliberately practical. slop-grader produces document scores and a list of flagged lines together with instructions that you can paste directly into an AI agent to fix the document. The tool flags its outputs as prompts so that agents can draft the fixes. This matters because a line-level flag makes it much easier to see exactly what needs fixing instead of rewriting an entire document from scratch. One product detail worth knowing is that when a rule fires on a line, the tool does not explain why it thinks the rule was violated. The maker's suggested workaround is to create separate rules for each check; the AI agent receiving the output is then very good at inferring the underlying problem from the flagged lines. Under the hood, slop-grader runs on Jev, available at typesafe.ai, which is described as a new AI model that is different from an LLM. Jev is a so-called System One model specialized in answering structured questions. That specialization is what makes the rule-by-rule approach practical: every rule is matched against every line separately, and because of the model's design that evaluation stays both cheap and fast. Checking a document takes seconds and costs less than a cent. Because evaluation happens on a remote model, the maker notes that text is evaluated on an external AI server, which is a relevant consideration when you decide what to run through the tool. The practical benefits follow directly from that design. Grading is consistent because the same rules are applied to every document and every line, so the standard does not drift between reviewers or between sessions. Feedback is precise because it arrives as flagged lines rather than a general verdict, which narrows the editing work to the specific places that broke a rule. The pipeline is fast and inexpensive enough that running a full check on a document takes seconds and costs less than a cent, so it can be part of a regular workflow rather than an occasional deep review. And because the output is written as instructions for an agent, the handoff from "this line is wrong" to "here is a sharper version" is automated rather than manual. Day-to-day, the maker describes using slop-grader to catch AI filler in launch copy, to strip buzzwords from landing pages, and to score narrative flow in launch emails. Those examples show the range: launch copy is checked for filler, landing pages are checked for buzzword density, and launch emails are graded on whether the narrative flows. Beyond those, the ruleset model supports SEO checks on content, review of legal clauses and disclaimers, and tone-of-address enforcement such as keeping German "Du" versus "Sie" consistent across a document. In each case the workflow is the same: run the document through your ruleset, read the score and the flagged lines, then paste the flagged output and instructions into an AI agent so it can draft the fixes. Getting started has a few stated requirements. You need Node.js installed on your machine, and you need an account with either TypeSafe or OpenRouter, since the evaluation runs on Jev. The tool is open source and is distributed as a CLI, and it is listed as free. The project lives on GitHub, and the maker has also published a skill for creating custom rules in the repository. It was launched on Product Hunt and is categorized as a command line tool, with topics covering writing, advertising, artificial intelligence, and GitHub. Text is evaluated on an external AI server, which users should factor into what documents they submit. The takeaway is that slop-grader reframes text review as a linting problem for prose. By turning what you care about into plain-language questions, evaluating them line by line and document-wide with a fast, low-cost structured-question model, and emitting flagged lines alongside agent-ready instructions, it replaces subjective rewriting with a score, a precise list of problem lines, and a clear path to an automated fix.