Category Focus/categories/automation
Automation AI Tools
Discover and compare the best automation AI tools and software. Browse 600+ curated tools with reviews and rankings.
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Foley is a voice-first to-do list that turns spoken thoughts into tasks with the details already in place. You say what needs doing — “I called the dentist,” “Change Sam's pull request review to 4 PM,” “Add bread to the shopping list” — and Foley works out the task, the project, the date and the reminder before anything lands. The product is for people who want to get a thought out of their head quickly, without doing the filing admin that normally comes with it. Foley runs on Mac, Windows, the web, iPhone and Android, with a reduced capture experience on Apple Watch (watchOS 10 or later) and Wear OS watches (Wear OS 3 or later). Most task apps assume you will stop what you are doing, open an app, create a task, choose a project, set a date and pick a reminder. Foley starts from the opposite premise: the thought arrives in a handful of words, and the system should do the work of turning it into something actionable. The site frames this as “Out of your head. Into your day.” Rather than forcing you to translate a half-formed thought into a structured record on the spot, Foley files the task with a project and a time, keeps the original words as part of the record, and only asks a question when it genuinely cannot tell what you meant. When plans change, you say so and it moves the task rather than leaving you to unpick and rebuild the list by hand. Capture is the centre of the product. A shortcut brings Foley to you wherever you are working, and you can say a sentence or several things at once — for example, “Add two tasks: buy milk, and call the electrician.” Foley works out the tasks, the dates and the project from what you said, and you review what it understood before the tasks land in your list. On the phone the capture sheet listens while you talk, then shows what it saved with the project and the time already set, and says so out loud in one sentence, by the same rules on every device. The words stay on the screen whether or not the voice plays, and when voice replies are off the app says why. The watch strips capture back further: tap the mark, say what needs doing, and it goes to the same list as everywhere else. While the microphone is open the wordmark pulses and the clock counts up, so waiting never looks like broken; you stop when you are done and nothing asks you to confirm. When Foley is not certain, it asks rather than guesses. If two tasks could fit what you said, it asks which one you mean, so you stay in charge of the change. On the watch, a question like “Which area, and how many people?” arrives with the likely answers as chips you can tap or say; because a short answer is where recognition is least reliable, what you said is read back before it goes anywhere, and you can send it or say it again. Capture history keeps the original words beside what Foley changed, so you can review the result and undo a capture when you need to. Alongside projects, Foley keeps simple lists for shopping, packing and little things to remember — add another item, tick things off, or say “Add bread to the shopping list.” Day to day, tasks from across your projects come together at your Daily desk, where you can open one, change a detail or check it off. The Timeline groups what is overdue, what is due today and what the week holds across every project, so the day is one glance, and a calendar view shows the shape of the day so that when plans change you can move a task or change its time while keeping the rest of the day in view. Projects give each part of life a home: folders keep related projects together, and projects hold the work and its context. Task context connects related work, keeps notes and attachments with the task, and shows the dependencies that explain what comes next — “The invoice to Acme is blocked by the board deck.” Planning turns the list into a plan: ask Foley to plan your day around the tasks already there, follow the plan back to the work, and adjust it as you go. For shared work, you can share a project through your Foley account so tasks and comments stay with the work, and in Your setup you connect your tools, choose your capture shortcut, and decide which projects AI may use as context, picking either Foley-managed AI or bringing your own key. The approach running through all of this is that Foley asks instead of guessing and shows its work. What it saved appears on the capture sheet with the project and time already set, it speaks a one-sentence result by the same rules on every device, and the original words sit beside the change in history. Signed in, the phone, the desk and the browser share one list, and the watch shows the same day in the order the service sends it. AI sits behind capture and planning rather than in front of it: you can use hosted, Foley-managed AI, or bring a personal key from a provider such as OpenRouter and pay that provider for usage. You also control which projects the AI may use as context. The outcome Foley is built for is a lighter head and a clearer day. Instead of holding a thought until you can write it down properly, you say it and move on, with the project, date and reminder already worked out. Instead of an undifferentiated list, you get a Daily desk and a Timeline that show what is late, what is due today and what the week holds. Instead of being told what to do by a system that guessed wrong, you get a question and you give an answer. And instead of a plan you have to maintain by hand, you can ask for a plan and change it in a sentence when the day shifts. Concrete uses the product describes include emptying your head in one pass — “Add two tasks: buy milk, and call the electrician” — and reviewing what Foley understood before the tasks land. It covers errands and chores with times: “Water the plants on Thursday evening” is filed as a task on Thursday at 18:00 in the Home project, and “Post the parcel” sits in Errands at 12:30. It handles changed plans with “Change Sam's pull request review to 4 PM,” disambiguates with “Complete the report expenses task,” and answers “What should I work on first?” from the list — for example naming an overdue parking permit renewal before a parcel due at 12:30. It keeps small checklists, links blocked work such as an invoice held up by a board deck, and captures from the wrist on an Apple Watch or Wear OS watch, including follow-up questions such as which area and how many people. Foley is for anyone who wants fast capture across the devices they already use. Desktop task management is free: a home for your tasks on your computer, local desktop task management, no sign-in needed to get started, and the option to bring a personal AI key for AI features, with your AI provider charging for personal-key usage. Foley Pro is Foley-managed AI with less to set up: hosted AI without a personal key, one account across devices, and monthly or annual billing, at $8 per month after an introductory $4 for the first month applied at checkout. Signing in is what enables hosted AI, sync and sharing, and the site notes that website pricing may differ from store prices. The desktop app runs on Mac and Windows, the mobile app on iPhone and Android, and the watch apps on watchOS 10 or later and Wear OS 3 or later. Taken together, Foley is a voice-first to-do list that carries a thought the whole way from the moment it is spoken to the moment it is planned and done: tasks filed with project, date and reminder; questions when the intent is unclear; history to undo a wrong call; the same list on Mac, Windows, the web, iPhone, Android and your wrist; and a free desktop tier or Foley Pro for managed AI and sync.
MePersona is an AI unified inbox built for content creators, designed to help them answer fan comments and direct messages without living inside a dozen apps. It drafts replies in the creator's own voice, gathers fan DMs from twelve platforms into a single place, and waits for approval before anything is sent. Its stated purpose is simple: stop drowning in DMs and reclaim personal time while still giving every fan a real answer. MePersona works in English and Spanish, so bilingual creators no longer have to choose which half of their audience receives a real response. Creators build audiences on multiple platforms, and each platform brings its own stream of comments and DMs. The result is fragmented attention: messages spread across X (Twitter), Instagram, TikTok, YouTube, Telegram, Discord, LinkedIn, Bluesky, Facebook, Mastodon, WordPress and Pinterest, with Twitch, WhatsApp and Reddit listed as coming soon. Answering every fan personally costs hours, while replying only to some leaves other fans feeling ignored. MePersona's positioning is that replying to every fan yourself costs hours of personal time, and its pricing page notes that Premium costs less than a dollar a day. Rather than replacing the creator with generic bot replies, MePersona is presented as a way to keep a real voice present at scale, with the creator retaining final control over what is sent. Everything in MePersona runs off a single AI persona. Training it is quick: the creator reads a short sample aloud and MePersona maps their real voice from that sample, then learns to talk, write and post like them. The Product Hunt description adds that the persona improves with every reply the creator approves and that it scores how much each draft sounds like the creator. Because every other part of the product runs off this one brain, the inbox, the vault, the marketing agent and any connected tools all draw on one consistent voice rather than separate systems. The unified inbox brings fan DMs from the supported platforms into one place. For each message, MePersona prepares a drafted reply, and the Product Hunt description notes that it remembers what each fan said last time, so context carries across conversations. Nothing posts until the creator hits Send. Every AI-drafted reply, post and campaign waits in the Review Queue until it is approved, so the creator can check each draft rather than publishing automatically. Once the persona has earned trust, auto-reply can be switched on for the routine messages the persona is sure about, while anything less certain still goes through review. This staged approach is what the FAQ contrasts with ordinary DM automation. Beyond the inbox, MePersona includes an auto-tagged content vault that stores material, alongside a Marketing Agent that plans campaigns in the creator's voice. Paid plans include one free Marketing Agent plan per month. Vault storage scales by plan, from 10 GB on Free through 100 GB on Premium, 500 GB on Pro and 1 TB on Pro Elite. Premium and above also add post scheduling across all platforms, a unified inbox and an analytics dashboard. Scheduling means the creator can plan posts from the same place they plan marketing instead of moving between tools, and the analytics dashboard gives visibility into how that activity performs. The persona is not locked inside MePersona. A Persona API, included with the Pro Elite plan at 1,500 AI calls a month, lets creators connect it to tools they already use. The documented scenarios include pointing an autonomous agent or runtime at the persona so it acts in the creator's voice, building n8n and Zapier workflows that pull new fan messages and draft replies that sound like the creator, powering a chat on the creator's own website so it talks like a person rather than a bot, and letting agencies and builders add voice-matched drafting inside their own tools and dashboards. Discord bots are also named as a possible connection point. A Creator Studio is described as on its way and marked coming soon. It is framed as create it, post it, answer it, all in your voice. The Create step generates images and videos, or remixes content already stored in the Vault. The Post step adds captions, music and templates, then schedules the result from the same place marketing is planned. The Reply step covers what happens when fans respond: the persona drafts replies in the creator's voice and the creator approves them. Studio is stated to work with a MePersona account. Because it is labeled coming soon, these capabilities are announced rather than currently available. MePersona's approach is a chain of small confirmations. First the creator trains a persona by reading a short sample aloud. The persona then produces drafts for comments, DMs, posts and campaigns, and each draft is scored for how much it sounds like the creator. Those drafts wait in the Review Queue, which makes approval the default state and publishing an explicit action. Approvals feed back into the persona, which the Product Hunt description says improves with every reply the creator approves. Only once a persona has demonstrated reliability does the creator enable auto-reply for routine messages. The FAQ positions this against plain DM automation and also addresses whether fans will know it is AI, whether the account could be banned, and whether the product can create content as well as reply to it. The stated outcome is time back. The pricing page argues that Premium costs less than a dollar a day, while replying to every fan yourself costs hours. Creators who adopt it can answer every fan rather than a subset, which as the Product Hunt description puts it stops bilingual creators from choosing which half of their audience gets a real answer. Being present across platforms at once means fans on Instagram, TikTok, YouTube or Discord are not deprioritized because the creator only had time for one app. The Review Queue protects the creator's output, since no unvetted message leaves the account, and auto-reply absorbs the routine traffic once trust is established. Concrete workflows appear throughout the site. A creator opens the unified inbox, sees a fan message with a draft already prepared, checks that it sounds right, and sends it. A bilingual creator runs the same flow in English and Spanish without splitting the audience. A creator with a large following switches on auto-reply for routine questions while keeping other messages in review. A Marketing Agent plans a campaign in the creator's voice, and the resulting drafts wait in the Review Queue. A developer connects the Persona API to n8n or Zapier so new fan messages are pulled and drafted automatically, or embeds a chat on their own website that speaks in the creator's voice. Once Creator Studio ships, the same pipeline extends to images, videos and captioned reels. MePersona is built for creators across niches, with Beauty, Fitness, Gaming, Music, Cooking, Lifestyle, Education and Comedy named on the site, plus a short five-question quiz that asks what kind of creator you are. Pricing starts with a Free plan at $0 that includes an authentic persona, 300K tokens per month (roughly 300 replies), two platforms, 10 GB of vault storage and post scheduling, with no card required. Premium is $29 per month or $299 per year with 2.5M tokens (roughly 2,500 replies), unlimited platforms and 100 GB of storage. Pro is $99 per month or $999 per year with 10M tokens (roughly 10,000 replies) and 500 GB. Pro Elite is $149 per month or $1,599 per year with 25M tokens (roughly 25,000 replies), 1 TB of storage and an API key. Paid plans begin with a 7-day free trial and offer a full refund within the first 30 days. Enterprise pricing is custom. Payments run through Stripe with Apple Pay supported, and the project has a Discord community. MePersona combines a unified inbox, a trainable voice persona and a review-first approval flow so creators can respond to every fan on every platform without losing their voice or their time. One persona powers replies, content, campaigns and connected tools, and the creator always hits Send.
Ghost Core is a personal AI computer from Ghost, described on its website as the personal AI computer. It runs AI models entirely on device, connects to your apps, files, and devices, builds context on your life over time, and works continuously to help you, not just when you ask. The site frames the product as Intelligence you own, and states that everything you do with Core is run locally using models on-device, so that no data ever leaves your home and your AI is owned entirely by you. It is aimed at people who want a private, always-available assistant that lives in their home rather than somewhere else. The problem Core addresses is presented on the site in two parts. The first is reactiveness: the site states plainly that Core doesn't wait to be asked, that it understands what is happening in your life and thinks proactively, and that it understands what to take action on without waiting for a prompt. Much of the value of an assistant disappears if the user has to think of the right question first. The second is ownership and privacy: the site's repeated claim is that intelligence stays yours on Core, that everything is run locally using models on-device, and that no data ever leaves your home. Together these two ideas — proactive help and local, owned intelligence — define what Ghost says makes Core different from assistance that lives elsewhere. Why it matters to Ghost's audience is stated in the copy itself: your AI is owned entirely by you, and the longer you use Core, the better it gets because context accumulates rather than resets. Local intelligence is the first of the three pillars the site highlights, alongside continuous context and a proactive agent. According to Ghost, Core runs its models entirely on device, and everything you do with Core is processed locally. That design is offered as a guarantee rather than a setting: the site says no data ever leaves your home, and that your AI is owned entirely by you. Practically, this is the difference between a device that sends what it learns about you somewhere else and a machine that keeps it in the room. Ghost also lists no subscription among the headline product facts and offers 30 day returns, so the ownership pitch extends to the commercial model as well as the technical one. Continuous context is the second pillar. Core runs 24/7, continuously taking in the context of your life, and it can connect to anything, from your apps, files, and screen history to wearables and smart-home devices. The site's stated consequence is simple: the longer you use Core, the better it gets. Rather than starting each interaction from zero, Core accumulates an ongoing picture of routines, activity, and surroundings. The examples shown on the page illustrate the texture of that context — a higher recovery score than usual, the observation that you work out more on early mornings, a package arriving today, groceries running low, rain tomorrow that may change the usual commute, four days of tacos in a row, stress peaking on Sunday nights, a refill pickup that could fit between gas and work, and a weekend trip that may need an earlier departure. The proactive agent is the third pillar, and it is the most visible part of the product. The site's framing is that Core doesn't wait to be asked: it understands what is happening in your life, thinks proactively, and understands what to take action on without waiting for a prompt. The kitchen scenario on the homepage shows how this plays out in a concrete setting. Core combines what it has learned about you with what it can sense nearby and produces short, timely nudges — you said you wanted to eat more fruit, take an apple with you; you have already had two coffees today, maybe water first; and don't forget your keys. These are not requests the user typed; they are observations and suggestions generated from accumulated context and the moment at hand. How Core works overall is a combination of on-device models, continuous data capture, and connections to the surrounding environment. The site states that Core is compatible with your devices and connects over Wi-Fi and Bluetooth. Its context sources are grouped into three areas: Digital, which captures context from the apps you use every day, including email, calendar, finances, files, recordings, and browser; Home, which connects to the devices in the world around you, including light, fridge, speaker, and cameras; and Health, which learns from your routine, recovery, and activity through sources such as health records, Whoop, Oura, and Eight Sleep. All of that feeds a set of models running locally on the machine, which then acts as the proactive agent described above. The hardware is specified in detail on the site. Core ships with an NVIDIA RTX PRO 4000 Blackwell SFF Edition GPU with 24 GB of GDDR7 ECC memory and 432 GB/s of memory bandwidth, paired with an AMD Ryzen 5 7600 CPU with 6 cores and 12 threads, 64 GB of DDR5 memory, and a 1 TB NVMe SSD. The models listed as running on Core are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B, and Muse-Glimmer-30B. In other words, the components that would normally sit in a data centre are packaged as a single object for the home — a brushed steel tower shown on the site from the front, back, and underside, with vents, a power input, and a light edge that glows. The benefits Ghost claims follow directly from those design choices. Because everything runs locally, the user gets assistance without personal data leaving the home, and the AI is owned entirely by the user. Because there is no subscription, there is no recurring fee attached to continued use. Because context is continuous rather than session-based, the product is described as getting better the longer it is used. And because Core operates proactively rather than only on request, it can surface things the user had not thought to ask about — health and recovery observations, incoming packages, weather effects on a commute, or a reminder to pick up groceries. Several concrete scenarios appear across the site. In the kitchen, Core notices what is present and what you have said about your intentions, and prompts you to take an apple, drink water instead of a third coffee, or remember your keys. In a daily-planning context, it surfaces that a package is arriving, that groceries are running low, or that a refill pickup could fit between gas and work. Around health and routine, it points out a recovery score higher than usual, that you tend to work out on early mornings, or that you are most stressed on Sunday nights. For travel and weather, it flags that rain tomorrow may change the usual commute or that a weekend trip may need an earlier departure. Core is presented as a personal device for the home, priced at $3,499 with no subscription and a 30 day return window. Batch 1 is listed as sold out, and the site invites visitors to reserve for Batch 2 or to order now. Its context integrations span everyday app sources such as email, calendar, finances, files, recordings, and browser; smart-home hardware including light, fridge, speaker, and cameras; and health and sleep trackers such as Whoop, Oura, and Eight Sleep, plus health records. Connectivity is over Wi-Fi and Bluetooth, and the machine's stated purpose is to keep intelligence and control in the hands of the people who use them. Taken together, Ghost Core is a personal AI computer whose value proposition is ownership: on-device models, no subscription, continuous context that improves with use, and a proactive agent that acts without waiting for a prompt. The site's own summary line is the clearest statement of intent — Ghost is building Core, the personal AI computer designed to keep intelligence and control in the hands of the people who use them.
Skymir is a native Google Drive sync client built specifically for Linux. It mirrors your Google Drive folders to local disk, keeps them in perfect two-way sync, lets you open Google Docs, Sheets, and Slides with a double-click, and shows sync status in a native GTK system tray. It is designed for Linux users who need reliable access to their Google Drive files without relying on Google's own desktop client, which is available only for Windows and macOS. The product's main purpose is to provide a safe, fast, and fully integrated Google Drive experience on Debian-based Linux distributions. Google has never released a Google Drive desktop application for Linux. Google Drive for desktop runs on Windows and macOS only. This leaves Linux users without an official, native way to sync their Drive files to local disk. Many have turned to tools like rclone, which is excellent for bulk transfers but not optimized for keeping a working folder in sync with Drive's native documents. The creators of Skymir experienced this firsthand: a sync run using rclone's bisync deleted 1,437 of their Google Docs because the native Docs, Sheets, and Slides were treated as missing files. That event led to building Skymir from scratch with a safety-first approach, ensuring that Google Docs become shortcuts rather than "missing files," and that every deletion goes to a trash you can restore from on both sides. True mirror mode is the core of Skymir. It mirrors your entire Google Drive — or just the folders you choose — in perfect sync with local disk. You can edit on either side, and changes flow both ways. Full offline access is available, so you can work on your files even without an internet connection. Deletions propagate safely to the trash on both sides, with a 30-day recovery period and never permanent. A sync that would delete more than half your files is refused, preventing catastrophic data loss. Because Skymir is built from scratch on the Google Drive API, your files are real files on disk, not a virtual filesystem. Skymir generates .gdoc, .gsheet, and .gslides shortcut files for every native Google Docs, Sheets, and Slides file in your Drive. These shortcuts let you open the live document directly from your file manager. Double-click a .gdoc file in Nemo, Nautilus, or Dolphin and the document opens in your browser. This bridges the gap between local file management and Google's cloud-native documents without requiring you to manually navigate the web interface. It also means your Google Docs are never treated as "missing files" during sync, which was the root cause of the data loss that inspired Skymir. Quick, incremental sync keeps your files up to date without unnecessary work. When you save a file locally, it is on Drive within seconds. Changes made in Drive — whether on the web, your phone, or another computer — come down within about a minute. Skymir asks Drive only for what changed since the last check using the changes.list API endpoint, so it never rescans your whole Drive. This makes syncing fast and efficient, even for large Drives. The background daemon runs continuously, keeping everything in sync while the tray icon shows current status. Skymir also includes a Recent Activity window that lists downloads, uploads, deletions, and shortcut creations with timestamps, giving you a clear audit trail of sync actions. The native GTK tray is a real AppIndicator3 icon that matches your Mint, GNOME, Cinnamon, KDE, or XFCE theme. It shows sync status at a glance, so you can see whether everything is up to date. Quick actions are available directly from the tray menu: sync now, pause, open folder, and open settings. This provides a native Linux experience, unlike tools that rely on command-line interfaces or non-native toolkits. The tray also displays trial status and last sync time, keeping you informed without needing to open the main application window. Skymir offers two sync modes to fit different needs. Full Drive Sync mirrors every top-level folder in your My Drive and automatically picks up new folders you create later — the same model as Google Drive Desktop on Windows and Mac. Selective mode lets you choose exactly which folders to mirror, so you can leave large or noisy folders behind. You pick at first-run setup, and you can switch between the two any time in Settings → Folders. Either way, only your My Drive is mirrored; shared drives are not supported yet. Installation is straightforward: add the skymir.app apt repository or download the .deb directly. The app works on Ubuntu, Linux Mint, Debian, Pop!_OS, and other Debian-based distributions. Skymir's approach is built on safety, transparency, and independence. It is an independent third-party application built on top of Google's public Drive API, not affiliated with or endorsed by Google. Your Google Drive data moves only between your computer and Google — Skymir never sees or stores it. The app sends only a small amount of data to its own server: email address and OS version at setup, license key and a random machine identifier at activation, and a weekly "still alive" ping. No usage telemetry, crash reports, or file information is collected. There is no vendor lock-in: if you uninstall Skymir, your local files stay on your disk and your Drive content stays in Google Drive. Pricing is simple and transparent. Skymir costs $19 as a one-time purchase with lifetime updates and no subscription ever. A 14-day free trial offers full functionality with no credit card required. After the trial, you need to purchase a license to continue using the graphical app and background sync. Each license activates one machine at a time, and you can move between computers anytime via deactivation. A 30-day no-questions-asked refund policy is available. Email support is provided at support@skymir.app. Use cases for Skymir are varied. You can mirror your entire Google Drive for full offline access on a Linux laptop. Selective sync lets you keep only the folders you need, leaving large or noisy folders in the cloud. Writers, developers, and researchers can open Google Docs from their file manager with a double-click. Teams and individuals working across multiple devices can rely on quick incremental sync to keep files current. Anyone who has experienced data loss with other sync tools will appreciate Skymir's safety features, including trash recovery and the refusal to delete more than half your files. The app is tested on Linux Mint 22.3, Ubuntu 24.04.4 LTS, Ubuntu 22.04.5 LTS, and Debian 13 "Trixie" across Cinnamon, GNOME, and KDE Plasma. In summary, Skymir is the native Google Drive client that Google never built for Linux. It combines true mirror mode, double-click Google Docs shortcuts, quick incremental sync, and a native GTK tray into a single application. Built safety-first from the ground up on the Google Drive API, it protects your files with trash recovery and deletion safeguards. With a one-time price of $19, a 14-day free trial, and no subscription, Skymir offers a compelling, reliable solution for Linux users who need Google Drive sync that just works.
Cue is a free macOS app that turns your headphones into a focus switch. Put your AirPods in and your Mac turns on your Focus mode, blocks distracting sites and starts a timer; take them out and it is break time. It is made for people who work on a Mac and want a focus session to begin without opening another app, and its interface lives in the notch next to the camera. Cue is free, private and built for macOS 14 or later on Apple Silicon. Most focus tools wait for you to open them, choose a session and press start, which means the moment you actually need help is exactly the moment you have to remember to ask for it. Cue takes a different route: it starts from something you already do many times a day, putting your headphones in and taking them out. macOS also has no public way to set a Focus mode, so Cue sets up its own shortcuts once and then switches Focus for you with no more prompts. The result is a focus session that begins and ends with your ears instead of with an app window, and nothing to remember. Your ears start it, not an app. Cue starts when your headphones go in and ends when they come out. Any Bluetooth headphones work, and Cue tells them apart by itself; AirPods Pro, Max, 3 and 4 are the most precise because they report when they are actually in your ears. AirPods start the session when they are in your ears, while other headphones start it when they connect. While you focus, Cue blocks sites: built-in social, video and news lists, plus your own sites, which you paste as a comma-separated list and Cue splits for you. Apps you list are asked to quit. Both the sites and the apps come back when you take a break, so the blocking is scoped to the session rather than to a permanent setting. The product lives in the notch. A small dot and a timer sit next to the camera; point at the notch to open it and tap to pin it. On a MacBook Air M1, iMac, Mac mini or Mac Studio there is no camera cutout, so Cue draws a floating 190 × 32 pill instead, and if you plug into an external display the same pill floats at the top of the screen. Inside the notch you get now playing, with artwork and play, previous and next controls for whatever is playing, from Spotify to a YouTube tab, read on your Mac only. Notifications from any app land in the notch instead of the corner and tuck away by themselves. Volume and brightness show in the notch rather than as a big box in the middle of the screen. You can also give the notch your own colors: pick a preset such as Peach, Lilac, Mint, Sunset or Aurora, or hit Mix and Cue blends three to five colors into three options so you keep the one you like, and the buddies wear the new accent. Two tiny buddies hang under the notch and react to everything you do. They run while you work, nap on your break, dance when music plays and blush when you point at them. New in 2.6 they leave the notch: outside a focus session they roam your whole screen, walking on windows, climbing their sides, hanging from the menu bar and jumping between them, and you can pick one up and throw it or click it to cheer. When focus starts they jump back onto the notch. The loop button sprays 20 to 30 buddies out of the notch and the power button adds ten more on each tap, up to 60; loop again and they all go home. Skins are free, with all 17 outfits available from day one, and the skin you pick is worn on the screen too, with eleven dance moves that change every phrase and stay smooth on the beat. A music panel shows what is playing on the notch with artwork and controls, and the buddies dance to the beat if you let Cue hear it. Sound stays in reach with per-app volume, an output switch and mic mute on ⌃⌥M, plus an orange or green dot that shows when the mic or camera is in use. Open your AirPods case and a card shows the battery of each bud with one-tap connect. Small islands cover keep awake, Caps Lock and a nudge to unplug at full charge, and a single switch hides the notch from recordings. Face Unlock is an opt-in extra for Macs without Touch ID, offered for convenience rather than security, and the maker states plainly that a photo of you could open it. The mechanics are simple. Step one: put them in and focus starts, with Focus mode on, sites blocked and the timer running. Step two: take them out and break starts, after a 10 second grace period in case you were only adjusting a bud, Focus turns off and your sites come back. The break ends after 15 minutes by default, and you can set it anywhere from 5 to 60 minutes in Settings. Step three: see your day, with today against your goal, your streak, your longest session, your sessions today and the last seven days in one place. Cue also greets you by name and reminds you to drink water and rest. The benefits are mostly about removing decisions. There is no Dock icon and no window in the way, so Cue never asks for screen space; a dot and a timer are all you see until you point at them. Because the trigger is your headphones, starting a session costs zero clicks, and the site and app blocking is scoped to the session so nothing is permanently locked. Cue states that it is private and free: no accounts, no analytics, and your data stays on your Mac. It is made with Swift only, and the maker notes it is light on the machine, with two roaming buddies costing no more than the notch alone. In practice, Cue fits a few clear situations. If you put your AirPods in to start work, the session begins the moment your buds go in and the social, video and news lists close themselves; when you pull them out to grab a coffee, the sites come back and a break timer runs until the break ends. If you are writing, coding or studying with music on, the notch shows the track and its controls without a window to switch to. If you are on a Mac without a camera cutout, the same experience follows you as a floating pill at the top of the screen, so an iMac or Mac mini user is not left out, and if you are on an external display the pill floats there too. Cue is for Mac users who already wear headphones while they work and want focus to follow their ears: writers, developers, designers, students and anyone who loses time to social media, video or news sites. It requires macOS 14 (Sonoma) or later and the download is built for Apple Silicon. Cue is free, with no ads and no accounts. The app is not notarized by Apple, so the first launch needs a one-time confirmation in System Settings, Privacy & Security, and setup asks for Accessibility and Motion permission, with optional Full Disk Access if you want to pick your own Focus modes. Optional update checks are off by default, and the only data Cue sends is feedback you write yourself plus two anonymous one-time setup signals. Cue is a small, free Mac app with one big idea: your headphones are the switch. Put them in and the Mac focuses, take them out and it relaxes, with the timer, the blocking and the buddies all living in the notch. It is private, light and built for macOS 14 or later on Apple Silicon.
Microsoft-Decision-1 is a new decision-scoring model from Microsoft, available in Microsoft Foundry and through OpenRouter. It is purpose-built to deliver structured outputs that software can immediately act on, and it is designed for routing, classification, prioritization, verification, and workflow control. The model is intended to make it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Give Microsoft-Decision-1 a set of fixed options and it returns a calibrated probability for each option in a single pass, as structured output that software can act on directly. It is positioned as a model for fast decision-making, delivering top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models. Decision models are quickly emerging as an important new category in AI. Unlike LLMs, which are designed to generate text or reason through complex problems, decision models are purpose-built to deliver structured outputs that software can immediately act on. Microsoft frames this as a shift: once you understand the capability of making decisions and classifying things at very low cost with high performance, all kinds of useful tasks get unlocked. Traditional large language models are oriented toward generation and reasoning rather than acting as reliable decision engines, and Microsoft-Decision-1 is designed specifically for that gap. By focusing on structured decision tasks rather than open-ended text generation, the model aims to outperform both LLMs and other decision models on latency and quality, so teams do not have to choose between the flexibility of a general model and the reliability of a purpose-built decision component. Accuracy is a central claim for Microsoft-Decision-1. Microsoft reports that the model achieved the highest accuracy in its 36-benchmark comparison, spanning nearly 150,000 questions across benchmarks kept blind from training. The accuracy figure is reported as a mean score across 36 benchmarks, covering 147,137 questions. This breadth — dozens of separate benchmarks and well over a hundred thousand questions — is intended to demonstrate that the model's strength is not limited to a single task type but holds across a wide range of structured decision problems. For teams evaluating a model for routing or classification, that breadth matters because it suggests the model can be trusted across varied inputs rather than only on a narrow distribution of examples. Microsoft also notes that Strands-Decider 2B was scored as the average of the 23 of 36 benchmarks it could answer, illustrating how comparisons were handled for models that could not complete the full benchmark set. Latency is the second headline capability. Microsoft states that in its benchmarking, Microsoft-Decision-1 was the fastest measured, running 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up, and 35 times quicker than GPT-6 Sol. The latency figures are described as JevBench v1.6.1 adjusted median (p50), checked on October 7, 2026, with Microsoft-Decision-1 measured through Foundry in the same region. The Product Hunt listing echoes this with the claim that the model is about 35 times faster than GPT-6 Sol at median latency. Speed matters for decision tasks because routing, classification, verification, and workflow control often sit directly in the critical path of an application or agent loop. When a decision must be made on every request, a model that returns answers in a fraction of the time can change what is practical to build — for example, making it feasible to score many inputs cheaply and quickly instead of batching decisions or skipping them altogether. Calibration is the third dimension Microsoft highlights. In the benchmark comparison, Microsoft-Decision-1 placed third on calibration, 1.5 behind Jev. Calibration is defined in the source material as a measure where 100 means a model's confidence exactly matches how often it is right — in other words, a well-calibrated model's stated probability reflects its true likelihood of being correct. Because Microsoft-Decision-1 returns a calibrated probability for each fixed option in a single pass, downstream software can act on those probabilities directly, for example by applying thresholds, comparing options, or routing based on confidence. The model produces structured output that software can act on directly, rather than free-form text that must be parsed or interpreted. That combination — calibrated probabilities delivered as structured output in a single pass — is what makes the model suitable for use as a decision component inside larger systems. How Microsoft-Decision-1 works overall can be summarized from the source material: it is a decision-scoring model that is post-trained from Qwen3.5-9B. Rather than generating prose, it is given a fixed set of options and returns a calibrated probability for each option in a single pass, as structured output. This single-pass, fixed-option approach is the model's distinctive methodology. The model is available in Microsoft Foundry and through OpenRouter, so teams can access it through those platforms rather than deploying a bespoke system. Microsoft describes it as delivering top performance in latency and quality on structured decision tasks, and the benchmarking data presented alongside the launch compares it against both LLMs and dedicated decision models on accuracy, median latency, and calibration. For users, the stated benefits center on speed, cost, and reliability of decisions. Microsoft says the model makes it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Because it returns calibrated probabilities that software can act on directly, it reduces the need to translate model output into actionable signals. The combination of high accuracy across a broad benchmark set, the fastest measured median latency, and calibrated confidence is intended to give teams confidence when embedding the model in automated decision paths. Microsoft also emphasizes low cost: making decisions and classifying things at very low cost with high performance is framed as the capability that unlocks useful tasks. On Product Hunt, the model is listed at $0.042 per million input tokens, with free output. The source material names specific use cases for Microsoft-Decision-1: routing, classification, prioritization, verification, and workflow control. It is also described as a model for agents and workflows, and more broadly as a decision model for agent control. These use cases share a common shape — they are structured decision tasks where a system needs to choose among a fixed set of options and act on the result. Routing describes directing a request, item, or task to the right destination or handler. Classification describes assigning items to categories. Prioritization describes ranking or ordering work. Verification describes checking whether something meets a condition. Workflow control describes deciding how a process should proceed. In each case, the model returns calibrated probabilities for the available options in a single pass, giving the surrounding application or agent a structured signal it can act on. Microsoft-Decision-1 is aimed at teams that want to incorporate decision intelligence into existing applications, agents, and workflows. Its intended environments are those where a secure, trusted model of this kind is valuable. It is available in Microsoft Foundry and through OpenRouter, giving teams two routes to access it. The model is post-trained from Qwen3.5-9B, which is the base model identified in the source material. Pricing is described on Product Hunt as $0.042 per million input tokens, with free output, and the model is presented as making decisions and classifying at very low cost. The benchmarking in the launch material reports accuracy as a mean score across 36 benchmarks (147,137 questions), latency as JevBench v1.6.1 adjusted median (p50) checked October 7, 2026, and calibration on a scale where 100 means confidence exactly matches how often the model is right. Taken together, Microsoft-Decision-1 represents Microsoft's entry into the emerging category of decision models — models purpose-built to deliver structured outputs that software can immediately act on. Its value proposition rests on three measured dimensions: the highest accuracy in Microsoft's 36-benchmark comparison spanning nearly 150,000 questions, the fastest measured latency (2.5 times quicker than the runner-up H2O-Lightning-4B v1.1 and 35 times quicker than GPT-6 Sol), and competitive calibration (third, 1.5 behind Jev). By returning calibrated probabilities for fixed options in a single pass as structured output, and by being available in Microsoft Foundry and through OpenRouter, it gives teams a practical way to add fast, reliable decision-making to their applications, agents, and workflows.
ReSO AI is a generative engine optimization platform that helps brands get discovered and recommended when customers ask AI. It is built for founders, CMOs, and growth teams who want to turn buyer intent expressed in ChatGPT, Perplexity, and Google AIO into visibility, citations, and traffic. Rather than simply tracking where a brand appears, ReSO analyzes intent, competitors, content gaps, and technical issues, then turns those findings into priority actions that can improve how AI platforms mention, cite, and recommend the brand. Onboarding the ReSO agentic employee is presented as the starting point: a buyer searching on ChatGPT should receive an answer that recommends your brand. Buyer behavior is shifting toward AI-led discovery, and the product states that in 2026 AI will dictate discovery. Buyers increasingly research on ChatGPT rather than on traditional search result pages, which means a brand that is absent from AI answers can lose influence before a prospect ever reaches its website. Traditional SEO tools were built to track keyword rankings, not to decode the intent behind the questions people ask large language models. ReSO positions itself against that gap, offering a way to understand how LLMs interpret intent, what they cite in a category, and which technical signals limit a site's ability to be read and trusted by AI systems. The discovery side of the platform is built on a proprietary intent model that analyzes 30+ intent-driven search variables to decode what customers are looking for. It maps those variables to real buyer queries on AI and organizes them by stage, including Discovery, Feature Comparison, and Persona, where Discovery covers the exploratory phase in which people look for the best options, trends, and possibilities before narrowing down what fits their needs. Users can curate a list of 75 high search intent-based prompts, track brand share of voice across the entire awareness-to-conversion funnel, and map their visibility against competitors so they can see where they are mentioned and where rivals are recommended instead. Content creation is handled through citation intelligence. Each piece of content is built around signals that influence AI visibility: prompt trends, citation behavior, and content gaps across the category. The platform supports a consistent weekly publishing cadence, a 100% customized content strategy intended to make the brand a category leader on LLMs, and content created in formats that improve mention potential. This matters because getting cited by AI search engines requires clear, authoritative answers, strong entity signals, useful source material, and structured content that AI systems can interpret easily, so the content work is aimed at the sources and authority signals that AI platforms actually draw on. A third pillar is the technical engine, which audits the technical issues that affect how AI systems and search engines access, interpret, and trust a website. It delivers technical edits intended to increase LLM visibility, entity mapping and code-level changes to make the website AI-readable, and improvements to discoverability across search platforms. Because AI systems must be able to reach, parse, and trust a site before they can cite it, these fixes complement the content and prompt work rather than standing alone. Overall, ReSO replaces the work of an SEO agency with an agentic employee that handles the process end to end, and it is designed to replace agencies, consultants, and fragmented internal workflows with one system. Its methodology is to measure first, through an AI visibility check, then act on the gaps it finds in brand presence, content, technical setup, and competitive positioning, and then keep publishing and optimizing on an ongoing cadence. The platform states that it works alongside traditional SEO while extending visibility into AI search, combining content, technical optimization, authority, and AI visibility tracking so that both traditional search and AI platforms are addressed inside one search visibility strategy instead of treating AI search as a separate channel. Reported outcomes from the company's own case studies include a 3X traffic increase from LLM-driven search, a +38% month-on-month citation growth, a +25% signup conversion directly from ChatGPT referrals, and a +30% quarter-over-quarter visibility score growth. A publicly listed fintech in India, SMC Global, saw a 132% increase in clicks from ChatGPT, a 162% increase in website clicks up from 37,200, and a 516% increase in impressions up from 923,000. An HRTech EoR platform achieved a 12X increase in citations from LLMs in three months, a 364% increase in blog traffic from 824 to 3,825 monthly clicks, a 5x increase in AI mentions from 10 to 50, and a 41% increase in AI visibility up from 8%. A US-based LegalTech company recorded a 92% increase in brand mentions up from 288, a 60% increase in total traffic up from 23,400, and a 55% increase in impressions up from 2,080,000. Concrete workflows described in the content include improving a brand's visibility in ChatGPT by identifying the questions buyers ask LLMs and where the brand currently appears in those answers, then prioritizing the pages, topics, and site improvements most likely to increase mentions and recommendations. Another is getting cited by AI search engines by analyzing which sources AI platforms cite in a category, finding gaps in content and authority signals, and optimizing the website accordingly. A third is competitive analysis: measuring share of voice across intent-driven prompts, seeing where competitors are mentioned or recommended, and locating the buyer-intent stages with the largest visibility gaps. The Discovery stage content shows the exploratory questions buyers ask, such as the best budgeting apps for managing personal finances, the best fitness trackers for everyday use, or the best tools for automating B2B lead qualification, which illustrates the kind of prompt-level work the platform supports. ReSO is aimed at founders, CMOs, and growth teams across high-growth startups and enterprise, and it is described as trusted by founders and CMOs. Its content highlights B2B brands in particular, noting that B2B buyers increasingly use ChatGPT, Perplexity, or Google AIO to research solutions, compare vendors, and shortlist providers, and that the platform also serves verticals such as fintech, HRTech, and LegalTech. Access begins with an AI visibility check from the onboarding flow, and the brand offers a detailed AI visibility analysis across brand, content, technical setup, and competitive gaps, stated as worth $500 at no cost for the time being, with no credit card and no commitment required to get started. The platform notes that AISO, GEO, AEO, LLMO and similar terms overlap, with AISO focused broadly on AI search, GEO on generative engines, AEO on direct-answer visibility, and LLMO on large language models, while the underlying goal is the same: making a brand easier for AI platforms to understand, mention, cite, and recommend. In short, ReSO AI's core value proposition is turning AI answer engines into a growth channel: it shows brands how LLMs see them, why competitors are recommended instead, and what to change in content and on-site technical signals so that the brand becomes the one AI recommends to buyers.
Onepin is an AI voice production agent that turns any script into voice that is ready to ship: model-matched, validated and auto-fixed. Rather than replacing the text-to-speech engines a team already relies on, Onepin works on top of them, connecting more than thirty providers — including ElevenLabs, OpenAI, Google, Microsoft, AWS, Rime, Inworld, Fish Audio, MiniMax, Deepgram, Naver Clova, Cartesia and Murf AI — behind a single account. It is built for teams shipping AI voiceover in several languages, such as product videos, dubbing and courses, and its stated promise is straightforward: human voice, right words, every language, so you do not have to listen to every line twice. The problem Onepin addresses is not voice quality. As the site puts it, AI voices sound human now, but they still guess names from the spelling, and nobody has time to listen to every line. Addresses, prices, dates and abbreviations make every TTS model stumble — "St." can mean Saint or Street, "NW" can mean NorthWest or North West — and mispronouncing a brand, a drug or a name makes the whole line unusable. That risk multiplies when a team ships in several languages at once, because the definition of correct has to stay the same in US English, Spanish, Japanese, Korean and German. Onepin exists to remove that listening burden and the manual re-rendering that follows it. The first step Onepin takes with a script is to clean the text. A normalizer expands and disambiguates the material that trips models up: addresses, prices, dates and abbreviations are fixed before synthesis, in every language. The site's own examples show "3042" rendered as "thirty forty-two", "St." resolved as either "Saint" or "Street", and "NW" expanded to "NorthWest". Onepin describes this as the most common cause of misreads, and states that because it happens before synthesis rather than after, roughly half of voice errors are gone. The value is easy to see in practice: the model never has to interpret raw, ambiguous text, so it reads the line the way it was meant to be read in the first place. The second step is pronunciation. Onepin checks names against a four-million-word pronunciation dictionary that teaches the system how to say the hard words right, in any language. The dictionary covers brand and product names — the site lists AbbVie, Versace, Sade, Siobhán, Måneskin, Hermès, Givenchy, Björk, Saoirse, Stromae, Balenciaga, Rammstein, Röyksopp, Moët, Ng, Xóchitl and Hoegaarden — as well as complex medical and scientific terms such as semaglutide, esomeprazole, hydroxychloroquine, esophagogastroduodenoscopy and electroencephalography. Onepin frames this as the first thing generic TTS gets wrong: one mispronounced brand, drug or name and the entire line is unusable, no matter how natural the surrounding audio sounds. Once voice is generated, every line comes back scored. Onepin validates four dimensions on every line — naturalness, word accuracy, clarity and pronunciation — and it scores against your bar, not its own, applying one bar across every language. The site illustrates the step with a single sentence, "Your order has shipped", rendered in US English, Spanish, Japanese, Korean and German, each version queued for scoring. The point of the validation stage is that quality becomes measurable rather than a matter of taste: a team decides what passes, and Onepin holds each line and each language to that same standard instead of asking someone to judge by ear. The fourth step is repair. When a line misses — the site's example is "Siobhan" misread in "Hold the gate, Siobhan" — Onepin either regenerates the line or fixes just the word that missed, using the same model with new settings. Fixes run themselves, and the stated outcome is that you always get the best take. This matters because re-rendering a whole take for one bad word is exactly the kind of manual work that makes multi-language voiceover expensive and slow; targeting the single missed word keeps the rest of the performance intact while still correcting the error. Behind those four steps sits a routing layer that puts every top TTS model in one place. A script flows into more than thirty models behind one account and is routed line by line. Onepin also benchmarks so customers do not have to: the company measures quality continuously and picks per run, on the stated premise that no model wins every language. Onepin presents this as the answer to shipping at scale, and it doubles as protection against vendor risk — if a provider hikes prices, deprecates a model or shuts down entirely, the pipeline does not move. A team keeps the same workflow while the engines underneath it change. Onepin also addresses the commercial side of voice production. Every output is assigned to you and cleared for commercial use, and the Enterprise tier adds a negotiated IP indemnity, which the site frames as what shipping at scale, and answering to legal, actually requires. Billing is pooled rather than fragmented: you buy one credit volume, allocate it across workspaces and reallocate it anytime, and seats are free. That structure means one organization can fund several teams' voice production from a single purchase instead of negotiating separate subscriptions for each group, with the flexibility to move budget between workspaces as priorities change. The use cases Onepin highlights are the workflows where a wrong word is most costly. Its examples span gaming ("Slough off the poison before it spreads"), audiobooks ("He read the minute inscription above Hawthorne's tomb, then wept quietly"), advertising ("Book your stay in Yosemite before the summer rush"), e-learning ("Today we'll cover the reign of Louis XIV") and film ("Subtlety was never his forte") — lines that hinge on words generic text-to-speech reads incorrectly. The product's entry point invites visitors to type a script of up to 100 characters and see results across categories including Product, L&D, Healthcare, Support, Travel and Sports, which are the verticals where brand names, medical terms, dates and prices appear routinely. Onepin is aimed at teams producing AI voiceover in several languages — product videos, dubbing and courses — and says it is trusted by teams shipping voice at scale, naming HeyGen, Hyundai Motor Group, Sierra, Bland and Johns Hopkins University. It is free to start with no credit card required, with a Pro plan for teams that are already shipping, and the site directs teams to start free or contact the company. The takeaway is that Onepin is not trying to be another voice model: it is the production agent that sits between your script and the many models that can read it, cleaning the text, fixing the pronunciations, scoring every line and repairing the misses automatically — so what ships is voice you do not have to listen to twice.
Phonable is a smarter voicemail app for iPhone that replaces your carrier's voicemail. When you cannot pick up a call, Phonable handles both sides of that missed call: your caller hears your greeting and then gets a short text back explaining why you could not answer, while you receive the voicemail as a recording, a full transcript and a one-line AI summary. It is designed for iPhone users who miss calls because they are in a meeting, on the road, at the gym or away for the weekend, and who want callers to know what happened without changing anything about their phone number. Phonable keeps your own number, your SIM and your phone, and only steps in for the calls you do not answer. Carrier voicemail has not changed in decades. You get a beep, a message you never quite get round to listening to, and a caller left wondering whether you received their call at all. As Phonable puts it, a missed call leaves both sides hanging. The caller does not know why you did not pick up or when you will get back to them, and you have to dial in, press 1 and sit through messages to find out what mattered. Phonable treats the missed call as a two-sided problem and fixes both ends of it: the person calling gets an immediate, polite explanation by text, and you get the substance of any voicemail delivered in seconds rather than minutes. The first half of the fix is the automatic text back. The moment you miss a call, Phonable sends a short text on your behalf, even if the caller hangs up without leaving a voicemail. That closes the loop for people who would otherwise never know their call landed. Phonable ships with templates for every moment — starting points such as "In a meeting", "Driving" or "Back on Monday" — and you can write your own wording if you prefer. Messages are considerate by default: each caller receives at most one text per hour, so someone who tries twice is not flooded with replies. Because each mode carries its own text, what callers read always matches where you are — driving, at the gym or away for the weekend. Texts use the SMS credits in your plan and are not sent to landlines or unsupported countries. The second half is visual voicemail with transcription and AI summarisation. Instead of dialling in and pressing 1, every voicemail appears in the app with the recording, a full transcript and a one-line AI summary, so you know what a message is about in seconds. The AI summary arrives as a notification, before you have even opened the app. From there you can read it or play it: skim the transcript quickly, or listen to the recording at whatever speed you like. If the message needs a response, you can reply in one tap, either calling back or sending a message straight from the voicemail screen. A voicemail stops being a chore you postpone and becomes something you can absorb at a glance. Greetings are the third piece. If you are not a fan of recording yourself, you can type what you would like to say, pick a voice and a tone, and Phonable speaks it naturally. The available voices are Sarah (soft and reassuring, American), George (warm and trustworthy, British), Laura (bright and upbeat, American), Lily (clear and polished, British), Brian (deep and composed, American) and Charlie (casual and friendly, Australian), and each can be delivered in a professional, friendly, calm or energetic tone. If you would rather sound like yourself, you can record your own greeting in seconds. Either way, callers hear something that fits the moment rather than a default carrier message. Modes, introduced in version 2.0, tie the greeting and the text reply together into a single switchable state. A mode pairs what callers hear with the text they get back, so you can move from Workday to Driving in one tap. Modes can be kept on for a while — an hour, three hours or until tomorrow morning — or set on a schedule by choosing the days and hours, after which Phonable switches automatically. The built-in examples show how specific they get. In Workday's "In a meeting" mode callers are told: "Sorry I missed your call. I'm in a meeting and will call you back within the hour." Driving's "On the road" mode says: "I'm driving right now and can't pick up. I'll get back to you as soon as I've parked." Gym's "Mid-workout" mode says: "Mid-workout, can't pick up! I'll call you back within the hour." And Weekend's "Away for the weekend" mode says: "Thanks for calling! I'm away until Monday. For anything urgent, send me an email." Phonable works through conditional call forwarding, a standard feature of mobile networks. After you sign up, the app shows you exactly what to dial and walks you through three quick steps to turn forwarding on with your carrier — a process that takes about a minute — and you can switch it back at any time, at which point missed calls return to your carrier's voicemail. Forwarding is conditional, so Phonable only takes over in the situations where you cannot answer: when a call rings out while you are busy, when you are already on a call or tap decline, or when you are unreachable because of flight mode, a flat battery or no signal. If you do pick up, nothing changes at all; the call connects as it always has and Phonable is not involved. Beyond the core loop, Phonable adds a set of thoughtful details. Every call and voicemail sits in one timeline organised by name, and if someone hung up without leaving a message you can still see that they received your text. Callers show up with the name from your contacts rather than a bare number. Privacy is handled deliberately: your contacts stay on your iPhone and are never uploaded, and the company states that it never sells your data. Pro subscribers can upgrade to a dedicated Phonable line that only handles their calls, rather than sharing a community line. The app also follows your iPhone's light and dark mode with a calm design. The overall benefit is simple: callers are never left guessing, and you never have to dial in and press 1 to find out what a voicemail said. The use cases follow the modes. If you are in a meeting, Workday mode tells callers you will call back within the hour instead of leaving them with silence. If you are driving, Driving mode explains that you cannot pick up and will respond once you have parked — without you touching the phone. If you are mid-workout, Gym mode does the same in a lighter tone. If you are away for the weekend, Weekend mode redirects urgent matters to email until Monday. Someone who simply misses a call while out of signal still benefits, because the caller gets a text and any message left is transcribed and summarised for later. Anyone who screens calls, works across time zones or is frequently in situations where answering is impossible can let a mode handle the explanation while they stay present. Phonable is an iPhone app available on the App Store, and it is aimed at anyone who misses calls regularly and wants callers treated well without switching numbers. Pricing starts free with Test Drive, which includes greetings, voicemail and text backs, a monthly allowance of voicemails and texts, up to two modes and a shared community line. Basic, billed monthly or yearly, adds more voicemails and texts every month, up to five modes and modes that switch on a schedule. Pro, also monthly or yearly, is described as everything Phonable does with room to spare: the most voicemails and texts, unlimited modes on a schedule, AI voice greetings and a dedicated line that is only yours. Prices are shown in the App Store in your local currency, yearly billing saves 20%, and you can cancel anytime in your App Store settings. Phonable's value proposition is that a missed call stops being a small failure on both sides. Callers get an immediate text explaining why you could not pick up and when to expect you back, and you get every voicemail transcribed and summarised by AI, delivered as a notification you can read in seconds. Nothing changes about your number, your SIM or your phone, forwarding takes about a minute to set up and can be turned off whenever you like, and you can start free before deciding whether Basic or Pro fits how many calls you miss. For iPhone users who cannot always answer, Phonable turns the calls you miss into handled, explained moments.
Opposable is computer use for phones. It gives Claude Code, Codex, Cursor, any MCP client, or the agent built into the app the ability to use a real Android phone the way a person does: reading the screen, tapping, typing, swiping and opening apps in every app you are signed into. The Android app is out now as a 43 MB APK and needs Android 11 or later. It is built for people who already drive an agent such as Claude Code or Codex and want that agent to reach the parts of their life that only exist on a handset — messaging, banking, rides, delivery, shopping, calendars and the constant stream of two-factor codes. The product's purpose is stated plainly on its own site: give your AI thumbs. Most of your life runs through phone apps, and none of them have an API. Banking, rides, delivery, messaging, and the two-factor prompt sitting in the middle of every workflow all live behind app interfaces designed for human thumbs rather than for software. An agent can write code, search the web or summarise a document, but it cannot open a banking app, check an amount and stop before paying. Reval Labs describes Opposable as the layer that lets agents use those apps, on the phone you own and on phones the company runs for you. Computer use is coming to phones, and Opposable is building the hands. At the centre of Opposable is an agent that runs on the phone itself. Where most phone agents send your screen to a model in a data centre, Opposable's own agent runs on the device it operates, so it keeps working with the laptop closed or with no signal at all, and your screen never leaves the phone. It is small enough to download once over Wi-Fi, and it gets faster with every release. Three levels of intelligence are described on the site. With no model at all it handles about 40 everyday tasks — alarms, reminders, calendar, calls and texts, camera, settings, volume, maps and conversions — in 0 to 15 seconds, checking its own result on the screen. A 1.4 GB screen model looks at the screen and taps like you in any app, and picks from the screen's labels in under a second when it can. A 0.7 GB answer model replies to questions from web results. On recent Snapdragon phones the screen model's vision step runs on the phone's NPU; on a Galaxy S25 the company measured it at about a quarter of a second, roughly 16 times faster than on a laptop CPU. Opposable does not replace your agent; it gives it hands. Anything that speaks MCP can drive the phone. For Claude Code it is a single claude mcp add --transport http command, and Codex takes a few lines in its config file; Cursor works the same way through MCP. The app shows its address and a token, and the phone becomes a tool in your next session. There is also a Claude and ChatGPT connector: paste the URL https://getopposable.com/mcp into a custom connector in Claude's settings, or turn on Developer mode in ChatGPT and add it there with OAuth sign-in. Either way you land on Opposable's page, sign in, pick your phone and allow access. The connector is free for 14 days per phone and then part of Pro. Opposable is model-neutral: Claude, Codex, OpenAI-compatible endpoints and the company's own on-device agent all drive the same surface. Two capabilities make the phone usable as a tool. It sees: Opposable returns a screenshot plus the screen as a list of labelled elements with their positions — text views, switches and their coordinates — which is cheap enough to read on every step. It acts: taps, swipes, typing in any language, system keys, opening apps and links, and every call reports what it actually did. The action surface is small and explicit — tap_element, swipe, type_text, launch_app, press_key — so an agent can chain a handful of calls into a finished task. Tasks can also be handed over whole: a REST call to the phone's /api/do endpoint with a task string returns a status, a result and the number of seconds it took, so the phone does the work locally and only the result comes back. Connectivity is deliberately boring. The phone can be reached over USB, over the same Wi-Fi network, or from anywhere through Opposable's hosted relay; the phone keeps one outbound connection, so there is no port to forward. If you have no spare phone, you can rent a private virtual Android phone in the cloud, sign into your apps once through a browser viewer and connect your agent to its private HTTPS endpoint with one command. Cloud phones start at $19 a month. If you are on an iPhone, Opposable for Mac pairs with the iPhone as a Bluetooth keyboard and pointer and sees its screen over a cable or AirPlay, so the agent can tap and type in every app with no jailbreak and nothing installed on the phone; on Windows or Linux a small Opposable Stick does the Bluetooth part. The benefits are practical rather than abstract. Your agent stops at the edge of the phone and starts working inside it, in the apps you are already signed into, with the sessions, the accounts and the history you already have. Because the agent runs on the phone, tasks keep running when the laptop is closed or the signal drops, and the screen never leaves the device for a data centre. Common tasks are handled without any model at all, which makes them fast and free. Payments and purchases always stop and wait for your Approve tap on every plan: the agent does the twenty taps, you make the one that matters. And because a task Claude does once can be saved as a recipe, the phone can replay it on its own in seconds the next time. The site describes a set of concrete scenarios. Messages: Opposable reads a 2FA text the moment it lands and types the code where it is needed — its own example shows the code 482 913 being copied from Messages and used to sign in to a bank, before the same phone replies to a chat and books a ride. Bank: it opens your banking app, checks the amount and stops for your OK before paying. Chat: it replies in your chat apps in your voice, with the whole thread in mind. Rides: it books the ride, picks the pickup point and sends you the driver's ETA. Shop: it finds last week's grocery order and puts it back in the basket. Inbox: it archives, replies and moves invites into your calendar. Recorded runs on the site show an agent turning on dark mode and setting a five-minute timer in 50 seconds and 17 tool calls, and looking up Apollo 11's launch date and crew through Chrome in 118 seconds for $0.44. Opposable is free to use; you pay for what is annoying to host yourself. The Free tier includes the phone app, its MCP server and REST API, the model-free common tasks, unlimited tasks and recipe replays over USB or the same Wi-Fi, and iPhone support through Opposable for Mac in beta. Pro costs $8 a month ($79 a year, with founding members at $5 a month for life) and adds the hosted relay so Claude Code can reach your phone on mobile data or any Wi-Fi, scheduled tasks and webhooks while your laptop is closed, payment approvals from the phone's notification, an audit log, and recipes backed up and shared across up to three phones. Power is $29 a month for up to 10 phones. Cloud phones run from $19 a month for a sleeping starter phone, $49 for an always-on phone and $149 for three always-on phones. The audience is developers and power users already running Claude Code, Codex or another MCP agent who want those agents to reach phone-only apps, plus teams that need several phones for parallel agents or separate accounts. Opposable's pitch is short: give your AI thumbs. It takes the computer-use idea that has been applied to browsers and desktops and points it at the device that actually holds your accounts, using an on-device agent, an MCP server, a relay for remote reach and cloud phones when there is no handset to spare. The agent does the endless tapping; you keep the one tap that matters.