Chatbot AI Tools

Dotverse is a Mac app designed for people who already have a Dot in ChatGPT and want to bring that Dot to their Mac in a new form. The app's central idea is expressed in its title: "Your Dot. Now with a crew." Instead of offering a separate AI service or requiring an API key, Dotverse connects to an existing Dot through a sign-in window. Once connected, users can create a crew of custom Dots, each with a different role and personality, and each suited to a different task. The website gives three illustrative examples: a Dot for writing, a Dot for planning, and a Dot for learning. Dotverse is intended for Apple Silicon Macs running macOS 14 or later, and it requires an existing Dot in ChatGPT. It does not replace that Dot; it extends the way the user can organize and interact with it. The product addresses a practical tension: one Dot may be useful across many activities, but different activities call for different styles of help. A writing task may benefit from a candid editor that sharpens a draft while keeping the user's voice. A planning task may call for a pragmatic planner that turns a big idea into manageable next steps. A learning task may call for a patient tutor that explains tricky topics with examples. Dotverse responds by letting users create different Dots for different tasks, giving each one a role and a personality. The content frames this as forming a crew around the existing Dot, rather than starting over with a separate assistant. Because custom Dots share the existing Dot's memory and capabilities, the crew is connected to the same underlying Dot. That shared foundation matters because the user does not have to recreate context for every new role; the custom Dots operate with the same underlying memory and capabilities, while each has its own role, personality, and conversation view in the app. Requests run one at a time, according to the FAQ. The first feature group is the creation of custom Dots for different tasks. Dotverse lets users create different Dots for different tasks and give each one a role and a personality. The website describes this directly and provides examples: a Dot for writing, described as a candid editor to sharpen a draft while keeping your voice; a Dot for planning, described as a pragmatic planner to turn a big idea into manageable next steps; and a Dot for learning, described as a patient tutor to explain tricky topics with examples. These examples show how role and personality are tied to a task. The writing Dot is not merely a general assistant; it is framed as an editor with candor. The planning Dot is framed as pragmatic and focused on next steps. The learning Dot is framed as patient and example-driven. Dotverse does not limit the concept to these examples in the text; it presents them as "a few you could create," while the explicit capability is that each custom Dot can have a role and a personality for a different task. The second feature group is shared memory and capabilities. According to the website, custom Dots share the existing Dot's memory and capabilities. The FAQ adds that each custom Dot has its own role, personality, and conversation view in Dotverse, but they use the same underlying Dot and share its memory. This distinction is important: the roles and conversation views are separate in the app, while the underlying Dot and memory are shared. The FAQ also states that requests run one at a time. The product therefore presents a crew of differentiated Dots without claiming that each one is a separate AI service or a completely isolated assistant. The shared memory is described as a feature of the custom Dots, and the site explicitly says, "Your custom Dots share your existing Dot's memory and capabilities." This allows a user to move between task-specific roles while remaining connected to the same Dot foundation. The third feature group concerns connection, privacy, and local storage. Dotverse connects to an existing Dot through a sign-in window. It does not provide a separate AI service and does not ask for an API key. An internet connection is required to chat, and some features open in the connected Dot window. On the storage side, Dotverse keeps encrypted copies of conversations and drafts on the user's Mac. Messages are also sent to the connected Dot service. The FAQ notes that clearing local history does not clear Dot's own conversations or memory. This means the app maintains local encrypted copies while the connected Dot service also receives messages. The content does not describe the encryption method or the exact scope of local data beyond conversations and drafts. Those details are simply not stated. What is stated is that the copies are encrypted, that they are stored on the Mac, and that clearing local history has a limited effect: it does not clear the Dot's own conversations or memory. Dotverse works as a Mac app that the user downloads as a DMG. The website provides installation steps: open the downloaded DMG, drag Dotverse into Applications, then open it. After opening, the user signs in to their existing Dot and meets their crew. The app runs on Apple Silicon Macs with macOS 14 or later. It requires an existing Dot in ChatGPT. The website lists version 0.2.0 and links to release notes. The overall approach is to bring an existing Dot into a Mac app and then layer a crew of custom Dots on top of it, each with a role, personality, and conversation view, while the underlying Dot and its memory remain shared. The FAQ also notes that some features open in the connected Dot window, which indicates that part of the experience remains connected to the Dot service rather than being fully reimplemented inside Dotverse. The stated benefits follow from those mechanics. Users can create different Dots for different tasks rather than relying on one general-purpose Dot for every activity. They can assign roles and personalities that match the task at hand, such as a candid editor for writing, a pragmatic planner for planning, or a patient tutor for learning. Because the custom Dots share the existing Dot's memory and capabilities, the crew remains connected to the same underlying Dot. Each custom Dot has its own conversation view in Dotverse, which supports separate task-specific interactions, while requests run one at a time. The app also keeps encrypted copies of conversations and drafts on the Mac, and it connects through a sign-in window rather than an API key. These are the outcomes the content supports; it does not make broader claims about productivity gains or accuracy. The content describes concrete use cases through its examples. A writer can create a writing Dot, described as a candid editor, to sharpen a draft while keeping their voice. A user with a big idea can create a planning Dot, described as a pragmatic planner, to turn that idea into manageable next steps. A learner can create a learning Dot, described as a patient tutor, to explain tricky topics with examples. More broadly, the app is for creating different Dots for different tasks, so a user can maintain a crew of task-specific Dots and switch between their conversation views. The FAQ's note that each custom Dot has its own conversation view supports the idea of separate interactions per role, while the shared underlying Dot and memory keep the crew connected. The content does not list other use cases beyond these examples and the general task-specific framing. Dotverse is aimed at people who already have a Dot in ChatGPT and use an Apple Silicon Mac with macOS 14 or later. The website states these requirements directly: "For Apple Silicon Macs · macOS 14+ · Requires an existing Dot in ChatGPT." The app is distributed as a DMG download, and the site lists version 0.2.0 with a release notes link. The connection is made through a sign-in window to the existing Dot, and no API key is required. The content does not state pricing, subscription plans, or a free trial. It also does not list third-party integrations beyond the connected Dot service. The only technical environment details given are Apple Silicon, macOS 14 or later, an internet connection for chatting, and the existing Dot in ChatGPT requirement. The website metadata mentions joining a launch list, but the main page content focuses on the download and setup. In summary, Dotverse's primary value proposition is contained in its tagline: "Your Dot. Now with a crew." It takes an existing Dot in ChatGPT and brings it to a Mac app where users can create a crew of custom Dots with different roles and personalities. The examples—a candid writing editor, a pragmatic planner, and a patient tutor—show how the crew concept maps to different tasks. The custom Dots share the existing Dot's memory and capabilities, each has its own conversation view, and requests run one at a time. The app keeps encrypted copies of conversations and drafts on the Mac, connects through a sign-in window rather than an API key, and requires Apple Silicon, macOS 14 or later, and an existing Dot in ChatGPT. Dotverse is therefore a way to organize task-specific interactions around one existing Dot rather than a separate AI service.

Kiwi is a free, proactive AI assistant that lives in your messages. Rather than waiting for you to open an app and type a request, Kiwi keeps track of what is coming up in your life — your trips, plans, bills and the people in them — and texts you first at the right moment. When something needs doing, it does it on any app or website as soon as you say yes. It is available for iPhone and Android, works with your calendar and email, and always asks before it spends. The product is built for people who want the small jobs of daily life handled without opening yet another app, and its stated purpose is to help you spend less time on your phone and more time with the people around you. The problem Kiwi addresses is described on its site plainly: the flights, the bills, the replies, the birthdays — each one pulls you back to your phone. Every small task means opening an app and tapping through it, and the small ones tend to wait until later. Kiwi's answer is not another dashboard to check but a proactive assistant that brings those tasks to you at the right time and gets them done while you get on with your day. The site also frames the economics of the problem: a personal assistant costs about $67,000 a year — citing the average base pay of an executive assistant in the US in 2026, from Payscale, at $67,567 a year, or $1,299 a week — while Kiwi does the same small jobs, every week, for nothing. The first layer of the product is awareness. Kiwi reads your calendar and email, so it knows what is coming up. It texts you once, at the right time, with the small jobs already done, and anything that can wait comes in your morning summary. Examples given on the site include reminding you an hour before a call with the link, sending your ticket on the night with what to bring, and spotting a delayed flight and booking a car for when you land. Kiwi also supports an on-demand watch: you can ask it to keep an eye on something and it tells you the moment it happens. In the site's example, a user writes "Tell me if my flight to Rome gets any cheaper," and Kiwi later replies that the flight just dropped to €84, down from €112 on Monday, offering to send the booking link. A Sunday wrap summarises the week in one text. The second layer is action. When you say yes, Kiwi books, orders and replies for you on any app or website; when there is no app for something, it uses the website. It checks you in, moves a delivery, books a restaurant or a car. Several guardrails are stated: anything that costs money shows its price first, it only says something is booked once the company confirms it, and it never pays twice for the same thing. In one example, a parcel needs a signature while you are at the office until 6, and Kiwi asks whether to move the delivery to Saturday; after the reply "Yes please" it confirms the delivery was moved to Saturday morning and that UPS has confirmed it. In another, a Saturday dinner at 9 leads Kiwi to offer an UberX at about €14 or a Comfort at €18, book the pickup for 8:35, and promise to let you know when the driver is close. Control and privacy sit underneath all of that. Nothing happens without your yes: anything that costs money or speaks for you waits. Kiwi does put your own plans in your calendar and set reminders on its own, but spending or messaging someone always waits for your approval. You can reply with one word, and it only asks again if something is unclear or the price changes. Nothing written in an email or on a website can tell Kiwi what to do — only you can. Passwords are encrypted before they are saved using AES-256-GCM, the AI never sees them, and Kiwi types them for you. Data is never sold, there are no ads, no business can pay to change what Kiwi suggests, nothing is shared for marketing, and nobody at Kiwi reads your conversations. You can delete a single memory, or your whole account and everything in it, from the app. The overall approach is messaging-first. The entire relationship happens in conversation: in the Kiwi app, in Messages and in WhatsApp. You start by tapping Sign up and picking your phone, iPhone or Android; it opens Messages or WhatsApp with "Hi Kiwi" already written, you press send, and Kiwi asks a few quick questions to set you up. After each conversation, Kiwi goes back over it and fixes anything it got wrong or forgot to do. A future piece of hardware, the Kiwi Band, is described as coming later: a band for your wrist with no screen that buzzes when Kiwi needs you, with answers given by a tap or your voice so your phone can stay in your pocket. It is explicitly not available yet. The benefits follow from that design. Nothing sneaks up on you, because Kiwi texts first rather than waiting to be asked. Small jobs that would otherwise wait until later are brought to you at the right moment and finished while you get on with your day, so you spend less time on your phone and more time with the people around you. You also get confirmation-based certainty: Kiwi only says something is booked once the company confirms it, and it never pays twice for the same thing. The site's example receipt for a week shows what this looks like in practice — a reminder before the 10am call with the link, concert tickets the night before with what to bring, a delivery moved to Saturday with UPS confirming it, dinner for five booked with the restaurant confirming it, and a Sunday wrap of the week in one text — with ads none, selling your data never, paying to be suggested never, and no app to install. Concrete use cases recur throughout the site. A free Saturday afternoon prompts Kiwi to offer to plan something with the kids, or the whole day. A delayed flight prompts it to check you in, spot the delay, tell you the new landing time and book a taxi from arrivals. A free evening prompts it to book dinner somewhere you will like with friends. A money question turns into a standing watch — tell me when my flight gets cheaper — and Kiwi reports the drop. A parcel needing a signature while you are at work becomes a request to move the delivery to Saturday. A dinner reservation becomes a car to get you there on time, with prices shown before you agree. Anything that can wait is collected into the morning summary, and the week closes with a Sunday wrap. On integrations and availability, Kiwi starts with your calendar and email — Google Calendar, Gmail and Outlook — then extends to the rest you already use, like Slack, WhatsApp, Spotify, Uber and many more, and can use any website when there is no app for it. It runs on iPhone and Android, and you talk to it in the Kiwi app, in Messages and in WhatsApp. Pricing is simple: Kiwi is free, with no card and no plan to pick, and it is free during the pilot. There are no ads, your data is never sold, and no business can pay to change what Kiwi suggests. The takeaway Kiwi repeats is that it texted first and you said yes. It is a proactive assistant rather than a prompt box: it watches what is coming up, arrives at the right moment in the messaging app you already use, shows the price before it spends, waits for your one-word yes, and only claims something is done once the company confirms it — aiming to give you back the small jobs of the week and, with them, your attention.

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Yourcast is a Telegram bot that turns the messaging app into a podcast player. Rather than installing a separate podcast client, a listener opens a chat with @yourcastbot, searches for a show, taps to subscribe, and new episodes arrive as audio right in the conversation. The bot is aimed at people who already spend their day inside Telegram and want podcast discovery, subscription and listening to happen in the same place they already talk, read channels and share files. Yourcast 2.0 builds on that listen-in-chat model with AI summaries that carry timestamps you can tap to jump into the audio, the ability to ask questions about any episode, feeds that repair themselves, file support up to 2 GB, OPML import and payments in Telegram Stars. Podcast listening usually starts by leaving whatever app you are in. You open a dedicated podcast client, search for a show you just heard about, and manage a separate subscription list that lives apart from your conversations. Yourcast addresses that split. Telegram already holds chats, channels and audio files, so keeping a listener's shows in the same space removes a step between hearing about a podcast and following it. The subscribe flow is deliberately reduced to a tap: find the show, tap once, and future episodes are delivered as audio messages in the chat instead of waiting in another library. That matters most in the moment of discovery, when the intent to follow a show is strongest and any friction tends to end it. The core of the product is search and subscription inside a chat. A user searches for a podcast by name, reviews the results the bot returns, and taps to subscribe. From then on, new episodes of that show arrive as audio directly in the Telegram conversation, so playback happens where the notification already is. There is no separate library screen to open and no episode list to refresh: the chat itself becomes the feed. The bot also points to a channel, @yourcast_channel, and a chat, @yourcast_chat, alongside the bot itself, giving listeners a place to follow updates and talk with other users. Because everything happens in a Telegram chat, the same interface works across the Telegram apps a person already uses, with the bot link launching it directly. Version 2.0 adds AI summaries to the listening experience. After an episode is available, the bot can provide a summary of it, and that summary carries timestamps. The timestamps are tappable: tapping one jumps to the corresponding point in the audio. This turns a summary from a static block of text into a navigation tool. A listener can read the summary first to decide whether a long episode is worth the time, then jump straight to the segment that matters instead of scrubbing through the player looking for it. For shows where only one topic is relevant, or for episodes that run long, the combination of an AI summary and tap-to-jump timestamps compresses the decision of what to listen to and where to start into a single screen inside the chat. Beyond summaries, Yourcast lets a user ask questions about any episode. Instead of reading or listening to the whole thing to find one answer, the listener asks the bot directly about the episode in the chat. The second reliability feature is feeds that repair themselves. Podcast feeds can break, and Yourcast 2.0 handles it by repairing the feed rather than leaving the subscription dead. For a listener, that means a show they subscribed to keeps delivering new episodes without manual re-adding or troubleshooting, and the maintenance work that usually falls to the user is handled by the bot. 2.0 also lifts several practical limits. Files up to 2 GB are supported, so long episodes and larger audio files that would previously exceed a limit can be delivered into the chat. OPML import means a listener can bring an existing subscription list from another podcast app into Yourcast rather than rebuilding it show by show. Payments run through Telegram Stars, so upgrading happens inside Telegram without leaving the app or entering card details elsewhere. On the free tier, users get 10 episodes a week on demand, which sets a clear boundary between casual listening and heavier use. How the product works overall is intentionally simple: it is a bot, not a separate app. A user launches it from the bot link, lands in a chat with @yourcastbot, and interacts through messages. Searching, subscribing, requesting an episode on demand, reading a summary, tapping a timestamp and asking a question are all chat actions in the same thread. Because the bot lives inside Telegram, there is nothing to install beyond Telegram itself, and episode delivery uses the app's existing audio file handling. The website presents the product in a single line, the podcast bot that searches, subscribes and plays podcasts, with a Start Bot button as the whole onboarding path. The benefit is consolidation. Listening, subscribing and catching up happen in a place the user already opens many times a day, which makes following a new show a one-tap decision instead of a context switch. AI summaries with timestamps reduce the time spent deciding what to play and finding the relevant part of an episode, and asking questions about an episode replaces hunting through audio for a single answer. Self-repairing feeds remove a maintenance chore from the listener. OPML import protects existing subscriptions, 2 GB file support removes a size ceiling, and Telegram Stars keeps payment inside the app. The free allowance of 10 episodes a week on demand makes it possible to try the workflow before paying. Typical scenarios follow directly from those features. Someone sees a podcast mentioned in a Telegram channel or a group chat, opens @yourcastbot, searches the title, taps subscribe, and the next episode shows up in that same conversation. A commuter with a forty-minute window reads the AI summary of a long episode, taps the timestamp for the segment they care about, and listens to just that part. A listener with an established library in another podcast app imports it via OPML instead of re-adding shows one at a time. Someone curious whether an episode covers a specific topic asks the bot a question about it before committing to a full listen. And a heavy listener who runs past the free 10 episodes a week on demand upgrades through Telegram Stars without leaving the chat. Yourcast is built for Telegram users who listen to podcasts, from casual listeners who follow a handful of shows to heavier users who subscribe to many. The bot interface is available in English, Russian, Portuguese, Spanish, German and Hebrew, so the same chat workflow works for listeners across those languages. The project is open source under GPL v3, with GitHub listed among its topics, which matters to users who want to inspect or reuse the code behind a bot they rely on. Pricing follows a freemium model: a free tier with 10 episodes a week on demand, and paid upgrades handled with Telegram Stars. Yourcast 2.0's value proposition is straightforward: it moves podcast search, subscription and listening into Telegram, then adds AI summaries with tap-to-jump timestamps and episode questions so that the time spent with audio is time spent on the parts that matter. Combined with self-repairing feeds, OPML import, 2 GB files and in-app Telegram Stars payments, it turns a chat window into a complete, low-friction podcast workflow.

Tori is an AI support agent built by TalkJS for customer success teams. It learns a product in depth from content the company already has, including help centers, documentation, product pages, and company knowledge, and then delivers accurate answers to even the most complex customer questions. Tori runs in a help center, inside an app, or on a website through a chat interface that can be fully customized. Setup is presented as a matter of minutes: connect a URL, let Tori learn the product content, and embed the chat UI. Rather than replacing the support team, Tori works alongside it, answering what it knows and escalating what it does not. Customer success and support teams often hold product knowledge across scattered support content, while customers expect clear answers in the moment. A support queue makes customers wait, and when a conversation finally reaches a human, customers may have to repeat information they already shared. Tori is designed around a different approach: bring scattered support content into one dependable knowledge layer, use it to answer questions instantly, and hand off to a person only when human help is needed. The product explicitly positions accuracy as a priority, flagging uncertainty instead of filling gaps when confidence runs low. That matters because an answer that sounds confident but is wrong can damage customer trust. The first part of Tori's approach is knowledge. Teams connect their help center, docs, or product site by pasting a URL. From there, Tori learns the product, turning existing product content into grounded knowledge. The setup flow shows help content, website pages, guides, and updates being learned, along with a sitemap that stays up to date. The result is described as one source of truth: scattered support content is brought into a single dependable knowledge layer for every conversation. Because answers are grounded in the content the company already has, Tori can address complex product questions using the same material the support team relies on. Tori also stays in sync and can run in a help center, app, or website, so the knowledge behind the conversation is connected to the places customers already look for help. Tori does not simply return raw documentation. It first identifies what each customer needs and brings the right product context into the conversation. It then responds in your voice, using your tone, guidance, and rules so that every answer stays aligned with how your team supports customers. Customers receive clear, sourced answers in the moment without waiting in a support queue. Accuracy is handled with explicit boundaries: Tori knows its limits, flags uncertainty instead of filling gaps, and brings in your team when confidence runs low. This combination of grounded knowledge, brand-aligned responses, and visible uncertainty is central to the product's promise of accurate AI support rather than plausible-sounding guesses. When a customer needs a human, Tori escalates seamlessly. The conversation is passed to the team with context intact, and agents get the conversation and sources ready to continue without making customers repeat themselves. That handoff is a core part of the design: Tori answers what it knows and escalates what it does not. The experience is also customizable. Built on TalkJS, Tori lets teams shape the interface and behavior to match their product. They can use the ready-made chat UI, customize the experience, or connect Tori through the API, and they can embed the chat UI in docs, an app, or a website. This flexibility means the support agent can fit the surfaces where customers already interact with the product. Under the hood, Tori is built on TalkJS, which provides production-grade messaging, real-time delivery, and full conversation history as standard. TalkJS is described as the same infrastructure powering millions of real conversations across the web. That foundation supports the overall workflow: Tori understands the question, responds in your voice, answers instantly with sourced information, and escalates seamlessly when human help is needed. In practical terms, the product combines a grounded knowledge layer learned from existing content with a customizable chat interface and a context-preserving handoff to the support team. The stated result is support that never clocks out, delivered through infrastructure built for real conversations rather than a standalone answer box. For customers, the benefit is immediate help: clear, sourced answers in the moment instead of waiting in a support queue. For support teams, Tori keeps responses aligned with their tone, guidance, and rules, and it avoids filling gaps when it is uncertain, reducing the risk of inaccurate answers. When escalation is necessary, agents receive the conversation and its sources, so they can continue without asking customers to repeat themselves. Teams also gain control over the experience: they can customize the interface and behavior, embed it in their docs, app, or website, or connect through the API. Security features such as SSO, data controls, encrypted data, GDPR-ready controls, and ISO 27001 certified TalkJS infrastructure support the trust side of customer support. Tori fits several concrete support workflows. In a help center, it can answer complex questions using the documentation and guides the team has already published, giving customers a self-service path before they open a ticket. Inside an app, the embedded chat UI can identify what a customer needs, bring in the right product context, and answer instantly. On a website, Tori can use product pages as grounded knowledge and respond in the company's voice. For complex product questions, it can provide sourced answers and flag uncertainty when confidence is low. When a conversation requires a person, Tori hands it to the team with the conversation and sources attached, so the human agent can continue without a restart. This makes Tori relevant for teams that want instant first-line support while keeping human help available for the cases that need it. Tori is aimed at customer success and support teams that need accurate answers without compromising customer trust. It is built for teams that already have help docs, product pages, and company knowledge to draw from, and for organizations that want the agent to run in a help center, app, or website. The product is made by TalkJS and uses TalkJS infrastructure, with SSO, data controls, encryption, GDPR-ready controls, and ISO 27001 certification. Pricing starts with a free Trial plan with no time limit, up to 50 AI responses per month, one seat, and one agent. Basic is $35 per month for 300 AI responses, two seats, and one agent. Standard is $140 per month for 1,400 AI responses, three seats, and three agents, marked as best value. Growth is $550 per month for 6,000 AI responses, seven seats, and five agents. Enterprise is a custom plan based on team and support needs. Customers choose a plan when they sign up. Tori's core value proposition is straightforward: learn the product from the content a team already has, answer complex customer questions accurately and in the team's voice, and escalate to a human with full context when needed. By combining grounded knowledge, customizable chat, TalkJS messaging infrastructure, and enterprise-grade security controls, Tori offers customer success teams an AI support agent designed for accurate, trustworthy conversations rather than generic automated replies.

Cited answers, a compliance matrix, and a deterministic verifier that refuses to export what it cannot prove.

Actually Asked is an AI FAQ app for websites and Shopify stores. Rather than adding a floating chat bubble, it places a FAQ section directly inside your page, styled in your own fonts and colours, so it looks like part of the site. Visitors ask their question in their own words and receive answers taken only from your own pages, your help center and files you upload. The questions visitors ask most then join your FAQ on their own. The problem it addresses is a familiar one: businesses answer the same questions again and again by email. Sizing, shipping times, caffeine content, whether a jacket is truly waterproof, what a cancellation policy covers, what to bring to a first appointment. The answers usually already exist somewhere on a product page, in a help centre article or inside a PDF, but visitors either cannot find them or do not want to dig. Actually Asked is aimed at any site where people ask before they buy, book or sign up. The company puts the filter simply: if people email you the same questions again and again, it is for you; if nobody asks you anything yet, it is not. Three features carry the core experience. The first is an inline FAQ: a FAQ section that lives in your page rather than a chat bubble, and that is styled like your site. The marketing content shows the same widget rendered in the look of three different shops, a linen shirt store, a coffee roaster and an outdoor gear shop, each with its own typography and colours. The second is highlight to ask. Visitors select any sentence on the page and ask a question about it, which turns a specific line of copy, such as a sizing note or a care instruction, into an entry point for an answer. The third is the self-improving FAQ. The questions visitors ask most join your FAQ on their own, with nothing for you to approve. You can still edit, pin or remove any entry, and the product states that your edits are never overwritten. Answers are restricted to your own sources and nothing else. On the Sources page you connect your site, its pages, and on Shopify your products and store policies, which are read again every 7 days. You can add the address of a help center or knowledge base, and you can upload PDF or Word files such as size charts, care guides, manuals and price lists, including files that are not public. Answers can quote a file but never link to it. The onboarding view shows a site of 38 pages, a Shopify catalogue of 212 pages and four policy pages, all read at 04:00 with the next read scheduled for 04:00 the following day. The weekly gap report arrives as one email every Monday. It lists the questions asked most, the ones your pages do not answer, and what was added to your FAQ. A sample report shows 214 questions asked in the week, 87% of them answered and nine with no answer to write about. A separate leads feature captures visitors who leave an email address, so you have someone ready to reply to. Finally, an MCP integration lets you manage your FAQ by asking Claude, ChatGPT or another assistant, for example publishing FAQ entries through a tool call. Setup is presented as three steps. First, you add your domain, plus your help center or PDF and Word files if you have them; those sources are read, private files included, and your first FAQ entries are written from them. Second, you paste two lines, a div carrying your site key and an async script tag from the Actually Asked CDN, and the FAQ appears where you put it; on Shopify you add it as a block in the theme editor instead. Third, visitors ask, and your FAQ grows. The distinguishing choices are the absence of a chat bubble, the tight sourcing rule that answers come only from your pages, and the fact that popular questions are promoted into the FAQ automatically rather than waiting on a human editor. The outcome the product describes is a FAQ that looks like yours and learns from visitors. Popular questions are answered once and added permanently, so the same email does not need to be written twice. Because answers are grounded only in your own material, the reply a visitor sees reflects what you already publish. The Monday report gives a regular view of the questions your pages fail to answer, which points at content worth writing. Leads are collected for follow-up, and the whole FAQ can be maintained from a dashboard, the Shopify admin or an AI assistant. Four use cases are documented. Online stores, including Shopify, WooCommerce, Squarespace and custom shops, use it for product and policy questions such as whether a size runs small. SaaS and apps use it on pricing pages and documentation for plan and "does it do X" questions, illustrated by a per-seat pricing example where an annual discount question is answered from the /pricing page. Travel and hospitality businesses such as hotels, rentals and tours handle check-in, cancellations and what is included, illustrated by a room booking page answering whether breakfast is included from the /rooms page. Services and clinics, including agencies, clinics, studios and coaches, cover booking, pricing and what to expect, illustrated by a first-visit page that answers what to bring. Actually Asked was built first for Shopify stores but works on any website whose visitors have questions before they buy, book or sign up. Integration paths include a Shopify theme block, a WordPress Custom HTML block or footer script, a Webflow Code Embed, a Framer Marketplace plugin or pasted component, a Squarespace Code block, and static HTML, React, Next.js or custom templates. There is also a free gap-finder tool to find FAQ gaps and an MCP route for managing the FAQ through Claude or ChatGPT. Pricing starts free with 50 AI answers a month on one site, then $12 a month; every plan includes unlimited team members, and you pay for the answers you use and the sites you run. Actually Asked replaces the chat bubble with something more durable: a branded, inline FAQ that answers from your own sources and quietly grows with the questions visitors actually ask. It is free to start, live on your site in about ten minutes, and aimed at any business that keeps answering the same question by email.

Spinachi is a personal health manager that runs on WhatsApp. Rather than asking you to install and learn yet another app, it meets you in the messaging app you already open every day: you forward your lab reports to Spinachi on WhatsApp, and it reads them, sorts them, and keeps your health records — and your family's — in one place. From there you can see where you stand, how things are changing over time, and when your next test is due. Spinachi describes itself simply as "your health, finally in one place," and it is explicit that it is built for organising and tracking health information, never for issuing a diagnosis. The problem Spinachi addresses is a familiar one: health records do not stay in one place. A blood test arrives as a PDF in a family WhatsApp group, a thyroid panel is emailed by a lab, a knee MRI lives on a hospital portal, and vaccination slips sit in a drawer. When you want to know what your last reading was, whether things have changed since the previous test, or when you were last screened, you have to hunt through messages, downloads and paper folders. Multiply that by every member of a household and the picture gets messier still. Spinachi's premise is that the answer should live somewhere you already are, and that the record-keeping should happen for you rather than being another chore you have to maintain yourself. The delivery channel is the product's defining choice. Spinachi works through WhatsApp, so there is no app to download, no account to keep logging into, and no new interface to learn. You sign up for Spinachi and then simply forward your reports as you receive them. Because the entry point is a messaging thread, the workflow fits the way people already share medical documents with their families — the same PDF that lands in your chat can be passed straight to Spinachi instead of being lost in a scroll of messages. Once a report arrives, Spinachi reads it and sorts it. The website shows this organisation in action, with health records gathered together and individual files filed alongside labels such as Lab report, Prescription, Scan report, and Full body checkup. Example documents used on the site include Blood Test 2025.pdf, Knee MRI.pdf, Thyroid Panel.pdf, and Chest X-ray.pdf, sitting under folder-style headings for Health Records, Prescriptions, Lab Results, and Vaccinations. The point is not just storage but structure: instead of a pile of attachments, you get categorised records that can be found again when you need them. Beyond filing, Spinachi is designed to help you understand where you stand. It keeps your records in a form that shows how things are changing over time, so that a single reading stops being an isolated data point and becomes part of a pattern. It also tracks timing: Spinachi reminds you when a retest is due, so follow-up tests do not quietly slip past. This combination — a stored history, a view of change, and a nudge for the next test — is the core of what the product offers. The overall approach is deliberately minimal. You forward a lab report on WhatsApp; Spinachi reads it; Spinachi sorts it for you and your family; the record is kept in one place. There is no app to install, and there is no diagnosis. Spinachi states plainly that it never provides a diagnosis, which keeps its role clearly defined: it is a manager and organiser of the health information you already have, not a source of medical opinion. That boundary matters, because it means the product can be useful for storing and tracking records without making clinical claims. For users, the benefits follow directly from those choices. Records for the whole family end up in a single place rather than being distributed across chats, inboxes and drawers. You can see where you stand and how things are changing, which makes it easier to arrive at an appointment with context rather than guesswork. Retest reminders reduce the chance that a recommended follow-up is forgotten. And because everything happens inside WhatsApp with no app, the effort required to keep the record up to date is close to zero — the act of forwarding a file is the act of filing it. Concrete scenarios follow directly from the document types shown on the site. After a full body checkup, you forward the lab report and Spinachi files it so the results are not lost among other messages. If a household member has a thyroid panel done, the record joins a lab results history that can be compared against earlier tests. Scan reports such as a knee MRI or a chest X-ray are stored alongside the rest of the family's records instead of sitting in a hospital portal. Prescriptions are kept in their own place, and vaccination records are collected together so the household's paperwork is complete. Spinachi is aimed at individuals and families who want their health records organised in one place. Access is through WhatsApp, and getting started means signing up at spinachi.in/start. The service is presented without a download, which makes it approachable for people who do not want to manage another piece of software, and for households where one person ends up holding everyone's paperwork. The takeaway: Spinachi turns WhatsApp into a personal health manager. Forward your lab reports, and they are read, sorted and stored with your family's records in one place. You see where you stand, how it is changing, and when your next test is due — with no app to install and never a diagnosis. The value proposition is straightforward: your health, finally in one place, maintained with the smallest possible amount of effort.

Mira Tours is an AI tour guide that turns a walk through any city into a narrated audio tour. Mira tells you the story of what is around you as you walk, and every tour is researched and written for you, wherever you happen to be. It offers walking audio tours anywhere in the world, with narration chosen per tour. The product is aimed at people who want to understand the place they are walking through, whether they are visiting a city for the first time, living somewhere they have never fully explored, or simply dreaming about a destination from home. Most walking tours require planning: choosing a route, finding a guide, joining a group and following a fixed schedule. Mira takes a different approach, built around four ways to start walking and a promise that you can plan it out or plan nothing at all. You can shape a route in conversation, start walking and let Mira find something interesting near you, head towards a destination you were going to anyway, or walk a tour someone else has shared. Rather than one fixed route for everyone, every tour is researched and written for you, so the stories you hear at each stop relate to the walk you are actually on and to what you said you were curious about. The four ways to start walking cover different intentions. The first is making your own tour: you say what you are in the mood for and build the route together in a chat, adding, dropping and reordering stops until the plan feels right. The second is wandering: there is nothing to plan or pick, you tap start, Mira finds something interesting near you and keeps finding stops for as long as you feel like walking. The third is heading somewhere: if you are going somewhere anyway, Mira routes you there and narrates what you pass, and if you give an arrival time she paces the walk to make it. The fourth is walking someone else's tour: others have shared the best of their cities, so you can walk one near you or listen from home to a city you are dreaming about. Building your own audio tour works like planning a day out with a friend who knows every street. You tell Mira what you are curious about and build the tour together. You can say what you are in the mood for, whether that is hidden history, film spots or something the kids will love, and any idea works. Mira then proposes real places near you, in walkable order, and you add, drop and reorder stops until the plan feels right. In the example shown on the site, a walker says "I have an hour and I love hidden history", and Mira responds by offering a route through the oldest corners of Chinatown starting a short walk away: Portsmouth Square, Old Saint Mary's Cathedral, Waverly Place and Ross Alley, about an hour on foot. When the walker asks for coffee near the end, Mira adds Caffe Trieste. Then you just walk, and every stop is narrated as you arrive, researched and written for your tour. As you walk, stories arrive when you do: you can lock your phone, put it in your pocket and keep walking, with narration delivered as you reach each stop. Your interests shape what you hear at every stop, because you can tell Mira what you love and what you can skip, and every stop leans your way; a topics screen lets you turn subjects such as Legends and Ghosts up and Politics down. At a stop you can see what else a place has to offer, hear another story, or just keep walking. You can also ask anything, anytime, about something you just heard or something you just walked past. Ask your guide, get a real answer, and turn it into more audio if you want to keep going. Tours are also designed to be shared. You can share a link and everyone walking with you hears the same stops you do, live as you walk. They install Mira, tap the link and follow the whole tour without spending any of their own minutes, because walking along is free for your friends. Tours are narrated in 23 languages, chosen per tour: Arabic, Bangla, Dutch, English, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Marathi, Polish, Portuguese, Romanian, Russian, Spanish, Tamil, Telugu, Thai, Turkish, Ukrainian and Vietnamese. Overall, Mira's approach is to research and write each tour for the person walking it, whether that route was shaped in a chat, discovered stop by stop while wandering, or planned to finish at a particular place at a particular time. The main benefit is that you do not have to plan, book or study to get the story of a place. You can begin with no plan at all, or shape one in a short conversation, and hear researched narration at each stop instead of reading it. Because Mira keeps finding stops for as long as you feel like walking, a walk does not end when a fixed itinerary does, and you can stop whenever you like. You can put your phone away and simply listen as you arrive, and you can bend the tour towards your own interests at every stop, leaning into legends and ghosts or turning politics down. If you are walking with others, everyone hears the same stories live rather than one person reading aloud from a guide. Concrete uses follow directly from the four modes. A visitor with an hour and a taste for hidden history can ask for that in a chat and immediately receive a walkable route with real stops and a coffee finish. Someone already heading somewhere can have Mira route them there and narrate what they pass, with the walk paced to an arrival time. A local with no plan can tap start and keep walking while Mira finds something interesting nearby, and keep going for as long as they like. Someone dreaming about a distant city can listen from home to a tour others have shared, and a group of friends walking together can follow one shared link so everyone hears the same stops live. Anyone who wants to try it first can walk a real city in street view in the browser as Mira tells them about it, with nothing to install. Availability and pricing are stated plainly: your first 30 minutes are free, and after that you buy listening time when you want it, with no subscription and no monthly fee. Thirty minutes for a quick tour costs $0.99, two hours costs $2.99 and saves 24%, and five hours costs $5.99 as the best value. Time never expires, and it is only spent on narration and research you actually use. Prices are in US dollars, and the app shows your store's local price. Mira runs in the browser at mira.tours/wander as well as in mobile apps, with downloads on the App Store and on Google Play, and Product Hunt lists it under Android, Travel, Artificial Intelligence and Google Cloud Run. In short, Mira Tours is a personal tour guide in audio form: an AI guide that plans a route with you or finds one for you, narrates it stop by stop as you arrive, adapts to your interests, answers your questions, and can be shared so everyone walking with you hears the same stories.

Toby is an AI mock interviewer built to help people prepare for real job interviews. You paste a job description, and Toby builds the questions a real panel would ask — from the warm-up to the curveball — then interviews you on a Google Meet, Microsoft Teams or Zoom-style video call. Toby Higgins, the AI hiring manager, asks the questions, listens, and tells you exactly what to fix. It is aimed at candidates who have an interview coming up, whether that is a recruiter screen, a hiring manager conversation, a case round or a values interview, and its purpose is to let you practise the hard questions before they count so that the real interview feels familiar. Interview preparation usually means reading a generic list of questions, rehearsing alone, or memorising answers that collapse under pressure. Toby's stated approach is different: it is not a generic list of questions. Toby researches the company, maps every round and plays the interviewer, then shows you exactly what to fix. The product frames itself as "the interview before the interview." The idea is that useful practice should resemble the interview you have actually got, happen on the kind of call you will actually take, and produce feedback that tells you precisely what to change rather than vague encouragement. The site explicitly contrasts this with generic question lists, positioning company-specific research and round-by-round mapping as the core of the preparation. The first thing Toby does is build your prep brief so that you know the company cold. Tell it the role and it produces a picture of what the company does, how it makes money, what has happened lately and how the team works. The brief presents stage, size, HQ and business model at a glance. The worked example on the site is a Senior Product Manager, Payments role at Fernleaf: spend management for UK and EU small businesses, covering company cards, bill pay and expenses in one account, at Series B with £45m raised, roughly 200 staff, headquartered in London. The brief also lists recent news and culture signals, dated — for example, a new CFO hired in August 2026 from a listed payments company, and a June 2026 launch of bill pay in Ireland and the Netherlands. Finally, it suggests talking points worth raising in the room: asking how SEPA failure rates compare with Faster Payments, noting that interchange is most of their revenue so authorisation rate is a revenue metric, and referencing their memo culture by offering a short written plan for your first 90 days. The point of this section is that you arrive already knowing the business model, the recent context and the signals that show you have done the work. Toby then maps the interview process so you can see every round coming. It sets out each round, how long it runs and what it tests, and then lists the questions most likely to come up in each one. In the example this is a 30-minute recruiter screen, a 45-minute hiring manager interview, a 60-minute product case and a 45-minute values round, with six questions listed per round. Each question is tagged by competency and difficulty and marked when real candidates have reported it. So "What made you apply to Fernleaf, and why this payments role in particular?" is tagged customer focus, motivation, easy and reported, while "This role leads a squad of six engineers without line management. How have you led teams in that set-up?" is tagged cross-functional leadership, leadership, medium and reported. There is also a browsable library of company interview guides linked from the site. This gives you a structured view of what each stage is assessing rather than one undifferentiated bank of questions. The practice itself is designed to feel like the day. You pick the round — recruiter screen, hiring manager, case or values — and how long you have got: 10, 20 or 30 minutes, whatever you've got. You then choose a mode. Real mode runs it straight through, uninterrupted, like the day itself, with the full debrief at the end. Coach mode gives you a note or two after each answer and a chance to try again; coach notes only appear once you have finished talking, so you are not interrupted mid-answer. The call runs in a Google Meet, Teams or Zoom-style format so the real thing feels familiar, and you can have your camera on or off. Toby will wait while you think, and as the site puts it, he is very patient. Overall, Toby works as a three-step loop: tell Toby the role, take the call, get the debrief. Everything is anchored to the job description you pasted, which is what makes the questions and the feedback specific to the role rather than generic. Toby Higgins plays the interviewer throughout, and the model is built around reproducing the conditions of the real interview — the round type, the length, the video-call format — and then converting that performance into structured notes. The outcome is a debrief that tells you exactly what to fix. Toby gives you a score against the role's rubric — the example shows 60 out of 100 with the verdict "Solid examples, light on measurable impact" — and then breaks down every answer: what worked, what to fix, and a stronger version built only from what you said. Each competency is scored 1–5 with the quote behind it, for instance customer focus at 3/5 because the answer was a real example but had no numbers to size the impact. It also measures delivery: talk time at 35%, pace at 136 wpm against an ideal of 130–160, and one filler word, "actually", used once. Because the stronger version is built only from what you said, you are not handed a generic script — you get your own answer rebuilt, with brackets to fill in with your real details, such as adding the result as a number: from what, to what, over what time. The full transcript, question by question, is included as well. The overall promise is simple: walk in having done it already. In practical terms, the use cases follow the rounds Toby maps. A candidate with a recruiter screen coming up can run a short 10-minute coach-mode session to warm up on motivation and background questions. Someone facing a hiring manager round can run a longer session in Real mode to get the uninterrupted feel of the day, then read the debrief. A candidate with a 60-minute product case or a values round can pick that specific round and rehearse against the questions most likely to come up in it. Before applying or interviewing, you can use the prep brief to learn the company's business model, recent news and talking points. And after a bad answer, you can review the transcript and the stronger version, fill in the brackets with your real numbers, and try again. Toby is aimed at job candidates preparing for interviews, from those practising a recruiter screen through to people preparing for case and values rounds at specific companies. It runs on the web. On price, Toby is free to start: your first 5 minutes on a call are free, which is enough to warm up and see how the feedback works, and you save a card to see your debrief — it isn't charged. The free tier includes a full prep brief for your role, likely questions for every round, Real or Coach mode, and a debrief on what you answered. After that it is £0.50 a minute; you buy minutes in packs from £5, they are only used on a call, and they last 6 months. There is no subscription. As an illustration, a 10-minute mock interview is £10.00, and both modes, prep briefs and debriefs are included, and you can stop whenever you like. Toby's core value proposition is that it turns interview preparation into a realistic rehearsal against the specific role and company you are targeting, then converts that rehearsal into concrete, measurable fixes — pacing, filler words, talk time, structure and stronger versions of your own answers. Rather than hoping the real interview goes well, you walk in having already done it.