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Productivity AI Tools
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TIM PG is a Windows utility designed to make the use of artificial intelligence safer by protecting sensitive information before it ever leaves your computer's local environment. Presented on timsoft365.com under the heading Privacy Guard, it is described as providing intelligent data protection and security for the safe use of artificial intelligence. The product is built for anyone who wants to use large language models and other AI tools in their daily work but cannot risk exposing personal or confidential data to those tools. Its core promise is simple: you keep working the way you already work, while TIM PG handles the protection of sensitive text and documents in the background. The rapid adoption of AI assistants and large language models has created a new kind of privacy problem. To get useful answers, people routinely paste emails, contracts, reports and other documents into AI tools. Much of that content contains personal data and confidential business information, and once it is submitted to a service it is out of the user's control. TIM PG addresses this problem directly. Instead of asking users to stop using AI, or to manually rewrite every prompt, it removes the sensitive parts automatically before the text is pasted, and puts them back afterwards. The result, according to the product, is secure local data privacy for your workflows — without giving up the productivity gains of AI. The central capability of TIM PG is automatic masking of personal data from your clipboard. As you copy text, the utility detects the sensitive elements and replaces them with masked equivalents before you paste that text into a large language model. The AI then works with the anonymized version of the content, so the model never receives the original personal data. When the AI returns its response, TIM PG seamlessly restores the sensitive data back into it, so the answer you read looks exactly as it would have if you had pasted the original text. This round trip happens without the user having to learn a new workflow or remember to anonymize anything manually, which is what makes it practical for everyday use. Beyond the clipboard, TIM PG includes document anonymization for PDF and Office files. Documents are among the most common carriers of sensitive information — contracts, invoices, reports, spreadsheets and presentations regularly contain names, addresses, identifiers and other personal data. By anonymizing these files, TIM PG allows users to work with their real documents in AI-assisted processes while keeping the underlying personal data protected. The feature extends the same protection model that applies to clipboard text to the files people handle most often in business settings, so users are not limited to protecting only short snippets of text. The product also introduces Smart Bubble technology, which protects text segments locally on your PC. Rather than sending content to a remote service for processing, the protection is carried out on the machine itself. This is consistent with the product's overall design: TIM PG is described as strictly offline and 100% AI-free. It does not rely on cloud processing to decide what is sensitive or to perform the masking. For users in regulated industries or in organizations with strict data-handling policies, that local-only approach is a significant distinction, because it means the sensitive data never travels to a third party as part of the protection process itself. What makes TIM PG distinctive is its architecture rather than any single feature. It is a strictly offline utility that runs on Windows and is explicitly described as 100% AI-free. The anonymization and restoration logic runs locally, with no cloud component, which the product summarizes as no cloud, no leaked data. This stands in contrast to approaches that ask users to trust a remote anonymization service, or that depend on a hosted model to redact text. TIM PG instead sits between the user and the AI tool, intercepting the content on the way out, masking it, and then reversing the process on the way back. The user continues to interact with their chosen LLM as usual; the protection layer is simply present in the middle. The benefits follow from that design. Users can continue using AI tools for drafting, summarizing, analysis and research without manually stripping out names, contact details or other sensitive content first. Because the original data is restored into the AI's response, the output remains directly useful rather than being filled with placeholders that have to be re-matched by hand. And because everything runs offline with no cloud dependency, organizations gain a way to adopt AI assistance that is compatible with data privacy expectations. The stated outcome is straightforward: secure local data privacy for your workflows, with no leaked data. Typical scenarios center on the everyday task of pasting text into an AI tool. A user might copy a customer email or a case note into a large language model to get a summary or a drafted reply; TIM PG masks the personal data first and restores it in the returned draft. The same pattern applies to document anonymization: a PDF or Office file containing sensitive material can be prepared for AI-assisted review while the personal data stays protected. More broadly, the product fits any workflow where a person or team wants the assistance of an AI model but needs to keep the underlying information private — a common situation in business operations, administration and any role that handles personal data. TIM PG is a Windows utility, so it is aimed at desktop users rather than mobile or browser-based users. The site presents it as the Privacy Guard component of a wider product family: TIM, described as a next-generation business platform for business operations, and TIM TL, a set of smart helper tools for daily business processes. The company behind these products emphasizes its expertise and trust, stating that its expert team provides decades of development and IT security background to guarantee business stability. The timsoft365.com site also offers a way to watch a demo of TIM PG and to request a consultation. No pricing or technology stack details are stated in the available content. TIM PG is best understood as a privacy layer for AI adoption. It does not try to stop people from using large language models; it makes that use safer by masking sensitive data before it is pasted and restoring it afterwards, entirely on the local machine. With clipboard protection, PDF and Office document anonymization, Smart Bubble segment protection, and a strictly offline, 100% AI-free design, it offers a practical route to the productivity of AI without the exposure of cloud-based data handling.
Spaces is a desktop app that gives a team one shared space per project, where the people on the team and their AI agents work side by side in the same place. Chats, files, and routines live in the space rather than in one person's private thread, so context survives from one day to the next and is visible to everyone who is in the space. It is aimed at teams that already pay for an AI model and want that model's work to end up somewhere shared instead of scattered across separate laptops. The app installs like any other desktop program, is free to start, and needs no account until you want to bring other people in. The problem Spaces is built around is described plainly on the site: five people, five private chat histories. Everyone on the team has their own AI thread and none of them can see each other's. The project's context is scattered across browser tabs on separate laptops, so every new question starts by re-explaining the job. The result, in the site's own words, is that each person gets faster while the team does not. That is the gap Spaces targets. Individual AI assistants are useful, but the reasoning, drafts, decisions, and files they produce tend to stay inside a single person's chat. By making the project — not the person — the thing that holds context, Spaces lets the standing knowledge of a project accumulate in one shared location instead of being rebuilt from scratch each time someone asks a question. The core of the product is the shared space itself: one project, one conversation. Chats and files live in the space, not in one person's thread, so a decision made on Monday is still there on Thursday for everyone. The site shows a Product Launch space as an example, managed by a Launch Lead, with a Copywriter and a Researcher also working in it. Its chat list includes "Press list for launch week" with the Launch Lead, "Waitlist copy" with the Launch Lead, and "Battery claims vs launch brief" with the Researcher, each showing when it was worked on. Alongside Chats, the space carries Files, so the documents a project depends on sit next to the conversations about them rather than in someone's downloads folder. Specialists are how Spaces replaces a single general assistant with a small team of agents. The site's instruction is to hire agents, not one assistant: a researcher, a copywriter, a launch lead. Each agent keeps its own notes, and anyone in the space can put any of them to work. Each specialist gets its own instructions, memory, and tools, which is what allows it to research, draft, work through your inbox, or run a report. In the interface, agents are added and managed from an Agents panel that distinguishes Agents, Skills, and Tools, and the space lists who is in it under a line such as "Managed by Launch Lead · Also Copywriter · Researcher." The practical effect is that a task goes to the specialist suited to it, and the specialist's own accumulated context stays attached to that agent rather than being lost between sessions. Routines are the part of Spaces that keeps working when nobody is watching. A routine is described as something that runs on a schedule — check-ins, follow-ups, reminders — and the space shows them in a Routines panel with their state and timing. The example space lists three routines: a "Launch morning brief" that runs daily at 9:00 AM, a "Friday recap" that runs Fridays at 4:00 PM, and a "Waitlist follow-up" that is currently paused. Each entry shows when it will next run or when it last ran. The point of routines, as the site frames it, is that the space does the standing work and pings someone only when it needs a decision, so recurring project chores like morning briefs, follow-ups, and weekly recaps stop depending on someone remembering to ask. Spaces does not sell AI usage. You connect the ChatGPT, Claude, or Gemini account you already pay for, and Spaces becomes the place those models do the work — you are buying the space, not another subscription for tokens. The Providers panel shows Anthropic's Claude and OpenAI's ChatGPT as connected, with Google's Gemini through Google AI Studio available to add, and Ollama available to run a model on the computer itself. Keys stay on the machine, and you can switch provider per agent at any time, so one specialist can run on one model and another on a different one. The site is explicit that Spaces is not a reseller: the AI account belongs to the user, and the app is the workspace that account operates in. The architecture is deliberately split between the local machine and the cloud. Each person's agents, API keys, and connected accounts stay on their own computer and never go to the cloud. What the cloud carries for a shared space is the space itself — the chats, files, and routines — and nothing else. Shared-space content is encrypted at rest with a per-space key held in Google Cloud KMS, and only members of the space receive that key. Anyone who uses Spaces alone can keep everything on their own machine, because a cloud space is only needed when other people have to work in the same project. The built-in browser lets agents use the real web rather than a cached or limited view of it. They can research, click through pages, and stay signed in, and because the browser lives inside the app, the agent sees the page the user sees. The site illustrates this with a research example: a Launch brief and a Supplier spec at example.com/solar-backpack, with the space's Files containing "Solar backpack — claims" listing a 65W peak panel, a 20,000 mAh pack, and a weatherproof shell, alongside an instruction not to claim the product "charges a laptop in an hour." The scenario shows an agent reading a live supplier page and checking its claims against the brief and the files already in the space. A space starts as yours. Inviting someone turns it into a shared space that both people work in: the same chats, the same files, the same routines, with their agents alongside yours. The site defines a cloud space as a project space that lives in the cloud instead of only on your machine, so other people can work in it — and notes that you do not need one to use Spaces by yourself. Pricing is split across three options. Spaces itself is free: the full desktop app, unlimited spaces on your computer, specialists, routines, and playbooks, and your own AI keys with no account needed. Spaces Cloud is the paid tier for teams, priced at $10.99 per year per person, described as $0.92 a month and, in the FAQ, as $1.49 a month, and it adds spaces that live in the cloud plus shared chats, files, and routines, and the ability to invite anyone who has a seat, while keys and agents stay local. Enterprise is a conversation rather than a checkout: Spaces inside your own cloud, data that never leaves it, and help with the whole setup. Everyone who works in a shared space needs their own seat, because each person runs their own agents, so a team of five is $55 a year in total; anyone who only works alone stays free, and cancelling leaves the desktop app free on your own computer. Getting started follows three steps the site lays out. First, download Spaces — it is free and no account is needed, and it installs like any other desktop app. Second, connect your AI by pasting the ChatGPT, Claude, or Gemini key you already have; one provider is enough to begin. Third, open a space: name the project, put a specialist to work, and the space starts holding the context. Spaces runs as a desktop app on Apple silicon Macs and Windows PCs. The examples on the site point to a few concrete ways teams use it. A product launch is run from a single Product Launch space, where a Launch Lead, Copywriter, and Researcher share a press list, waitlist copy, and checks of battery claims against the launch brief. Research work happens in the built-in browser, with agents reading live pages such as a supplier spec and comparing them against files in the space. Routine work is scheduled rather than requested: a morning brief at 9:00 AM, a Friday recap at 4:00 PM, and a waitlist follow-up that can be paused and resumed. Teams that need everything inside their own infrastructure can run Spaces inside their own cloud through the enterprise option. Taken together, Spaces is a place where a project's chats, files, and scheduled work live together, and where each person's AI agents contribute into that shared context instead of into private threads. It keeps the AI accounts and keys people already pay for, keeps their agents on their own machines, and charges only for bringing other people into the same space. The value proposition is simple: the team, not just the individual, gets to benefit from the AI work being done.
Accordio is an AI back office built for people who run a business alone. It tracks your time, drafts contracts, proposals and invoices, gets them signed and collects the money, and it does all of it from a chat message rather than a dashboard. You connect it to Claude with a single URL, or text it on WhatsApp, Telegram or Slack. The product is aimed at consultants, designers, developers, coaches, founders, fractional CXOs and agencies of one who need the admin handled without hiring anyone. Most solo operators do not lose money because of their craft; they lose it to the paperwork around it. Hours go untracked, contracts are sent late, invoices sit unpaid and receipts pile up until tax season. Roma Bors, Founder at Deduxer, states the problem directly: he lost 50 thousand dollars to clients who never paid, so he built the app that makes sure it never happens again. Accordio exists to close that gap. Instead of subscribing to an e-signature tool, an invoicing app, a time tracker and a contract template library, the whole office ships inside one product that already knows your hours, your clients and what you are owed, so it never has to ask you for context before it acts. Time tracking is the foundation of the product. A Mac app tracks the day on its own, so you can ask what you worked on today and see what is still unbilled without ever starting a timer, while manual timers remain available when you want them. The tracker keeps a full history and produces client-ready timesheets, and it breaks the work day down into focus, meetings and breaks so you can see where the hours actually went. The tracker is open source on GitHub and free forever, and Claude and ChatGPT can both read and log your time. Billable hours convert into an invoice in one click, which is the real point: hours captured at the moment they happen do not get lost or forgotten at the end of the month. Documents are the second pillar. Describe a project and Accordio returns a complete contract with the right clauses and payment terms; an AI advisor flags gaps before you send it, and clients sign without creating an account. Those e-signatures are legally binding and meet the US ESIGN Act and EU eIDAS rules, with a timestamp, IP address and a full audit trail behind every signature. Proposals follow the same flow: Accordio drafts the scope and price, sends the proposal and tracks opens, and when the client says yes the proposal turns into a contract and an invoice without retyping anything. Forms and questionnaires capture leads and intake, and an estimate widget can be embedded on your website. Pitch decks round out the document set, and 12 themes, your own logo and colours and editable clauses mean clients see your brand rather than Accordio's. Existing contracts can be imported from Google Docs and elsewhere and become editable, signable documents. Invoicing and payments are where Accordio is most differentiated. An invoice can be generated from a project, from tracked time or from a contract milestone, and every invoice carries a pay page with your bank details configured for the US, EU and UK. Bank transfer payments carry no fees and no commission, and if you want to accept cards you add your own payment link and it appears on every invoice. Connect your bank through Plaid and incoming deposits are matched to open invoices on their own; when a match is unclear, Accordio asks you rather than guessing. Overdue invoices are chased automatically with payment reminders, and the accounting layer keeps expenses, a profit and loss report and a tax report ready so your books never need a bookkeeper. Snap a receipt and it is categorised, marked deductible and filed to the right report, with bank reconciliation happening continuously in the background. Around the paperwork sit the meeting, inbox and notification features. Booking links let clients find a slot on your calendar without back-and-forth email, and a meeting recorder auto-joins calls, transcribes everything and extracts action items so you can stay present instead of taking notes. Meeting briefs arrive before calls, and follow-ups are drafted afterwards. Accordio also manages your inbox: it reads new mail, drafts replies in your voice and flags the messages that need you. Proactive alerts push morning briefings, overdue reminders and deadline warnings before you even ask for them, and each client gets a branded portal with their contracts, invoices and files behind a magic link, so there is no password and no signup for them. The delivery mechanism for all of this is the MCP connector. Accordio speaks MCP, so pasting a single URL into Claude, through Settings, then Connectors, then add custom connector, gives Claude 30 tools covering your hours, clients, unbilled totals, invoices, contracts, calendar and tasks. Claude can see your business and act on it: ask what you promised a client on Tuesday's call and it answers from the recording. It can draft the contract, log the hours and prepare the invoice, while sending, signing and payments stay in the app, so Claude proposes and you approve. The connector is free forever and also works in Claude Code, Codex and ChatGPT, and the same assistant is reachable by plain text on WhatsApp, Telegram and Slack. Setup is described as a two-minute job with nothing to migrate, and the product is also available in the browser and as a Mac menubar app. The benefit is that the admin stops being a separate job. Because Accordio already holds the invoice, the contract and the hours, it does not ask you for context when you give it an instruction; it just does the work. You open one product instead of four subscriptions, and it replaces an e-signature tool, an invoicing app, a time tracker and contract templates with a single place. Payments arrive without chasing, receipts file themselves, calls are followed up and mornings start with a briefing rather than a backlog. The tracker stays free forever, so time tracking and asking an AI about your hours never carries a cost, and the paid tier adds the money documents and the agent that sends and chases them. Concrete workflows show how this plays out. A consultant asks who still owes them and Accordio reports that a client is 12 days late on a 2,400 dollar invoice and offers to send the reminder. A designer describes a retainer and gets a draft on their usual terms, sent to the client for signature. A solo operator asks Accordio to bill the hours for last week and it invoices 14.5 tracked hours, sends the invoice and attaches bank details. Someone asks for 30 minutes with a contact next week, Accordio sends the booking link, and the accepted slot lands on the calendar. A receipt for a software subscription is messaged in and filed under expenses, matched to the card charge. For founders, the pattern is broader: customer contracts described and signed, each customer billed on their own cadence with deposits matching themselves, books kept without a bookkeeper, and every call recorded with action items filed and follow-ups drafted. Accordio is built for people who run a business alone: consultants, designers, developers, coaches, founders, fractional CXOs and agencies of one, with dedicated solutions for software companies. It integrates with Google Workspace, Slack, Notion, Asana, Trello, Linear, GitHub, Figma, Zoom and more, and the site references Gmail, Google Calendar, Google Drive and 160+ apps, with bank connections handled through Plaid. Pricing is simple: the Mac time tracker, timers, time entries, clients, projects and the Claude and ChatGPT connector are free forever with unlimited clients and projects, while the Legend plan costs 39 dollars a month, or 29 dollars a month billed yearly at 348 dollars a year, and includes every feature, unlimited e-signatures, zero commission and 10,000 AI credits a month. There is a 14-day free trial with no credit card, and if it ends, nothing is deleted. Accordio's core proposition is that the assistant you already talk to should be able to run the admin around your work. Track time, create and sign contracts, send invoices, get paid, and let one message do the rest.
sizeless is an AI-powered workflow for civil engineering that delivers precise 3D digital twins of open trenches and house connections, directly for GIS and CAD. The company describes its core promise simply: it turns a smartphone video of an open trench into the documentation utilities and contractors are legally required to produce — a 3D model, CAD/BIM plans, and the quantities they bill from. The product is aimed at network operators, civil engineering teams, district heating projects, and pipeline construction work, and it replaces a process that today takes months and a surveyor with one that the company says is completed in hours, from a video the crew films themselves. Underground infrastructure work has a documentation problem that is both expensive and time-sensitive. Once a trench is backfilled, the evidence of what was actually installed — pipe routes, couplings, house entries, third-party utilities crossing the excavation — is gone. Historically, capturing that evidence required waiting for separate surveying appointments before the trench could be closed, which delayed backfill, delayed billing, and delayed cash flow. Where documentation was handled manually, teams relied on manual sketches and created data silos that were hard to audit. Basement areas and other below-grade spaces made matters worse because GPS is not available there, so conventional positioning-based capture methods struggle. sizeless targets exactly these pain points by making documentation a by-product of work the crew already performs on site. The capture side of the workflow centres on SiteScan, the sizeless iPhone app. Field teams use a guided iPhone Pro workflow to capture properties, trenches, and technical rooms in minutes. The company emphasises that this requires no special hardware and no specialist training: the existing project team carries out the capture as part of the job. Because capture happens directly at the excavation, technicians can document house connections independently via smartphone, and trenches are backfilled immediately after the video is taken rather than waiting for a separate surveying appointment. SiteScan is distributed through the App Store, where it is listed as the Sizeless iPhone app. A single smartphone scan produces every deliverable the team already works with, described on the site as "one capture, every output". Those outputs fall into three families: 3D point cloud, 2D CAD, and BIM/GIS. The centimeter-accurate point cloud is a high-resolution 3D reconstruction of the scanned space and serves as the objective basis for earthwork volumes, dimensions, and audit trails. From it, sizeless generates industry-standard 2D CAD as-built plans in DWG and DXF for revision documentation, with the company noting that couplings and pipes are quickly identified and that measurement extraction is simplified. On the 3D side, sizeless produces digital twins of the pipe route including house entries, with seamless integration into GIS systems for future-proof planning and maintenance. Beyond the deliverables themselves, sizeless positions three core benefits for network operators. Quality and audit-proof documentation is provided as continuous 3D evidence that includes third-party utilities and house entries, and that works without GPS in basement areas. This eliminates manual sketches and data silos, and means construction errors can be identified before backfilling rather than after. Process autonomy means technicians document house connections independently via smartphone, so existing internal or external teams can handle a higher project volume through efficient workflows. Accelerated construction and billing follows from immediate backfill after the video: complete documentation is available weeks earlier, described by the company as "72h docs", enabling faster billing and cash flow. The workflow is presented as four steps. Step 01, trench capture, is standardized capture via iPhone Pro directly at the excavation — no special hardware and no extra appointments, carried out by the existing project team. Step 02, 3D point cloud, uses algorithms developed at ETH Zurich to generate a high-resolution 3D point cloud of the open trench, which the company describes as centimeter-accurate. Step 03, 2D CAD as-built plan, produces industry-standard as-built plans in DWG/DXF for revision documentation. Step 04, 3D model and GIS, creates digital twins of the pipe route including house entries and integrates them into GIS systems. The differentiator is therefore spatial AI for underground infrastructure: reconstruction algorithms combined with a capture method that needs nothing more than a phone the crew already carries. For the people doing the work, the outcomes stated on the site are concrete. Trenches can be backfilled immediately because no separate surveying appointment is needed, and complete documentation is available weeks earlier than the traditional route, which supports faster billing and cash flow. Documentation becomes audit-proof and GIS-ready, with continuous 3D evidence replacing manual sketches and disconnected data silos. Errors surfaced in the record can be addressed before backfilling. Because no specialists are required, technicians can work independently and teams can take on higher project volume. The site summarises the outcome as 72h docs, DWG/DXF output, and instant backfill. Several concrete scenarios appear in the material. Trench documentation during civil engineering works: a crew films an open trench with an iPhone Pro and the trench is backfilled immediately afterwards. House connection documentation: technicians document house connections independently via smartphone, including house entries, and those entries are captured in the 3D twin. District heating projects: the workflow is explicitly named for civil engineering, district heating, and house connections. Pipeline construction: automated as-built documentation is described for pipeline construction, where pipes and couplings must be identified for revision documentation. Network operation and maintenance: the resulting digital twins integrate into GIS systems for future-proof planning and maintenance of the pipe network. Property and technical room scanning is also mentioned as something the SiteScan app captures. sizeless is built for network operators and for the civil engineering, district heating, and pipeline construction teams that build and maintain underground infrastructure. The company states that it was founded by engineers from ETH Zurich and UC Berkeley and that it is backed by Y Combinator, ETH Zurich, UC Berkeley, Cambridge, and MIT. Integration points named in the content are GIS systems, CAD and BIM workflows, and industry-standard DWG/DXF file formats, alongside the SiteScan iOS app. The site's primary calls to action are booking a demo, seeing it in action, and requesting an in-person demo with a full name, email address, company name, and preferred demo date. Taken together, sizeless reframes as-built documentation as something a construction crew produces as it works rather than something a surveyor delivers afterwards. The combination of a guided iPhone Pro capture workflow, ETH Zurich-developed reconstruction algorithms, and outputs that drop into existing CAD, BIM, and GIS tools means the trench can be closed immediately, the documentation arrives in hours instead of months, and the resulting record is a continuous, audit-proof 3D twin that supports both billing today and planning tomorrow.
chat-recall is a tool that turns the conversation history your team has built up with AI coding assistants into a single searchable archive. It reads what your assistants have already written down — the chats, the plans, the task lists and the notes — and consolidates them into one searchable history. As the product puts it, Ctrl+F doesn't work on your brain, but now it works on your chat history: your team has done months of work with AI assistants, and none of it is searchable until chat-recall is installed. It is aimed at developers and teams who use several AI coding tools at once and want the record of that work to be findable rather than scattered. The problem chat-recall addresses is fragmentation. Claude Code, Codex, Cursor and OpenCode each keep a full record of the work you do with them, in its own format, and none of them can read the others. Across five AI coding tools that means months of chats, plans, task lists and notes sit in separate silos that no single search can reach. The result is work that gets redone because nobody can find the original decision, plus a quieter risk: passwords and API keys that were pasted into old conversations and never noticed again. Without a way to search across tools, both the value and the risk hidden in that history stay invisible to the team that created it. You start with a single command: npx chat-recall init. One command reads what your assistants already wrote and turns it into one searchable history. The steps the product describes are straightforward. First, your conversations: everything your assistants have written down, the chats, the plans, the task lists and the notes, across five AI coding tools. Second, passwords are removed on your own computer. Third, everything lands in one searchable history, available the moment it arrives. There is nothing to restructure by hand and no export to perform, because chat-recall reads the records the assistants have already created and assembles them into a single index. Privacy is handled locally rather than in the cloud. Passwords and secrets are removed before anything leaves your computer, and the vendor states that it only ever sees the last few characters of a removed value; the interface shows a masked preview such as the start of a token, a run of asterisks, and a short tail. Steps one through three of the pipeline happen on your own machine. That local-first design matters because the content being indexed is often the most sensitive material a team produces: live credentials, internal plans and unfinished work that nobody wants uploaded to a third-party search index just to make it findable later. Beyond organising history, chat-recall actively looks for secrets that leaked into old chats. Its security view groups every leaked credential by rule, and each row shows a masked preview of the value, a live-or-dead verdict on whether the key still works, the detectors that matched it, and how many sessions it appeared in, so you can see whether a key is still live and how widely it was exposed on one screen. The detectors know the key formats that the big services publish. If your company has invented its own format, you can tell chat-recall the pattern and it gets checked too, which is how custom internal credentials end up covered as well. chat-recall also gives your assistant access to the history itself. Your assistant searches it directly, so it stops asking what you decided last month, and one shared memory means decisions, findings and tasks live in one place rather than in a single tool's silo. The product lists tasks with proof, and what to fix next, among its outputs, and bugs turn into tasks on their own: each one shows up with the fix already sketched out, and closes itself once the problem is actually gone. Rules can be set once per project — mark a project a prototype or a live product, and every assistant that opens it plays by the right rules. Because teams rarely use one assistant on one machine, chat-recall includes a toolkit coverage matrix: six skills down the left and a column for each AI tool on each of two machines. A filled cell means the skill is installed there; an empty ring means it is missing, and clicking it copies the skill across with a sync-to-all action. Each row carries a coverage count so you can see at a glance which of your assistants has which add-on. The payoff is portability. A new laptop already knows everything once you sign in, with nothing to copy over by hand, and every add-on you have built up follows you to whichever assistant you pick up next, so trying a new assistant does not mean starting over. Overall, chat-recall works as a local assembly line rather than a cloud service. A single command reads the records that five AI coding tools have already written, strips passwords on your own machine, and produces one searchable history that is ready immediately. The same history, the same tools and the same rules are then available everywhere you work, across assistants and across computers. Three of the steps happen on your computer, and the product describes the flow as an assembly of your conversations, the removal of passwords, and one searchable history — with leaked passwords, what to fix next, tasks with proof and one shared memory all downstream of that assembly. The benefits follow from that design. Work stops being redone because the original decision is searchable, and your assistant stops asking about things you already decided. A new laptop is useful the moment you sign in, and a new assistant arrives with your accumulated add-ons already in place. Secrets stop being invisible, because leaked keys are surfaced with a verdict on whether they still work and how many conversations they turned up in. Bugs become tasks with a sketch of the fix and disappear on their own once resolved. And rules can be applied consistently, so a prototype and a live product are treated differently wherever they happen to be opened. Concrete uses follow the same pattern. A developer wants to find a decision the team made weeks ago and searches the shared history instead of scrolling through one tool's transcripts. A security-minded team runs the leaked-key check to catch credentials pasted into old chats and to see which of them are still live. Someone setting up a new laptop signs in and finds the whole history already there. A developer trying a new assistant keeps the add-ons built up over time. A bug found in a chat becomes a task with a sketched fix that closes when the problem is gone. And a project marked as a prototype is opened under the right rules by every assistant. chat-recall is built for developers and teams who work across multiple AI coding assistants and want a searchable record of that work. The named integrations are Claude Code, Codex, Cursor and OpenCode, with the site referring to five AI coding tools in total, and to add-ons or skills that can be installed per tool and per machine. Installation runs through npx chat-recall init, which places it in the Node.js command-line ecosystem, and the product links to documentation on how it works and on what your assistant can ask. The takeaway is that chat-recall gives a team back the searchability of its AI work. One command reads what Claude Code, Codex, Cursor and OpenCode already wrote, removes passwords before anything leaves your computer, and produces one searchable history that your assistant can search itself — while also catching keys that leaked into old chats and checking which of them still work.
Moji is a desktop application that opens Markdown files the way you would open a PDF. The project describes itself simply: double-click a Markdown file and start reading. Moji displays your document with clear typography, tables and diagrams, and keeps editing and export ready for when you need them. It runs on Windows, macOS and Linux, is free and open source, and requires no account. The product positions itself as a fast, clean and distraction-free way to read Markdown, with the stated purpose of making a Markdown file open the way a PDF does: with a double-click, instantly readable and with no setup required. On the project's site, the creator explains the motivation directly under the heading "why I built Moji": the goal was for a Markdown file to open the way a PDF does, with a double-click, instantly readable, with clean typography and no setup. The site adds that usability came first from day one, that Moji is lightweight and comfortable for long-form reading, and that it is simple enough to disappear while you read. Editing and export were added without losing that focus. The entire design intent is captured in the line "Less interface. More document." The problem being addressed is the friction between raw Markdown text and a comfortable reading experience: instead of configuring a toolchain or an authoring environment simply to look at a document, the user opens the file and reads it, and the more advanced capabilities only appear when they are actually needed. Opening and reading a document in Moji is deliberately flexible. The site lists four ways to open a file: a standard file dialog, drag and drop, file associations, and a multi-tab workspace. File associations mean a Markdown file can be opened by double-clicking it in the operating system, which is the behaviour the product is built around. The multi-tab workspace lets several documents stay open at once, so readers can move between files without returning to a file picker each time. Once a document is open, Moji renders a rich, secure preview that includes tables, task lists, footnotes, LaTeX, code highlighting, emoji and outline navigation. A synced outline keeps the document structure visible alongside the content, and a dark theme is available for focused reading. The interface uses subtle chrome and compact controls so that the content itself stays in the foreground. Editing is available but stays secondary to reading. Moji's editor is built on CodeMirror 6 and adds Markdown shortcuts, line numbers, history, and search and replace. The site describes this as precise editing covering code, shortcuts and search. The intent is that a user who opened a file to read it can switch into editing without leaving the reader or losing the distraction-free layout, then return to reading. Markdown shortcuts speed up common formatting tasks, line numbers help when working with code-heavy documents, history provides a record of changes within the session, and search and replace supports navigating and updating longer documents. Moji renders Mermaid diagrams without leaving the document. Valid Mermaid blocks in a file become responsive diagrams inline, and the site states that flowcharts, sequence diagrams, Gantt charts, class diagrams, ER diagrams and more are supported. Diagrams can be zoomed from 10% to 1000%, panned freely, or fit to view, and a minimap plus a dedicated diagram viewer help navigate larger charts. Each image can be exported individually as PNG. The rendered diagrams are described as self-contained SVG in HTML, PDF and PNG output. The site summarises this as turning code into diagrams instantly, which means a document that describes a system in Mermaid syntax can be read and understood visually in the same window. Exports are described as predictable. Moji can export to PDF for precise layouts, to HTML for web-ready output, and to PNG, which the site presents as suited to long documents. Exports preserve typography, diagrams and very long documents. The export dialog is intentionally simple: the user chooses a format and a layout. Because Mermaid output is embedded as self-contained SVG, diagrams that appear in the reader also appear correctly in the exported PDF, HTML or PNG file. Moji is a desktop application rather than a web service. Official installers for Windows, macOS and Linux are published directly through GitHub Releases. For Windows, an NSIS installer is provided for Windows x64 with automatic updates. For macOS, a universal DMG supports both Apple Silicon and Intel, with manual updates. For Linux, users can choose an AppImage with automatic updates or a DEB package for manual installation. The version referenced on the site is 1.0.7. Moji is free, distributed under the MIT license, and requires no account, so no sign-in or subscription stands between the user and their document. The stated benefits follow from that design. Because Markdown files open with a double-click, the tool fits into the same habit as opening a PDF. Because the interface is lightweight and uses subtle chrome, it is described as comfortable for long-form reading and simple enough to disappear while you read. Because the preview supports tables, tasks, footnotes, LaTeX, code highlighting, emoji and diagrams, technical and structured documents render correctly rather than as raw text. Because editing and export are built in, a single application covers reading, light authoring and sharing. And because the interface is available in Portuguese, English, Spanish, Japanese, Chinese and Russian, readers can use the product in their own language. Several concrete scenarios follow from the features the site describes. A developer who receives a README or technical document can double-click the file and read it immediately with code highlighting and correctly rendered tables. A writer or note-taker can open a long Markdown document in the multi-tab workspace, follow the synced outline, and use the dark theme for extended reading sessions. Anyone working with technical documentation can embed Mermaid blocks and view flowcharts, sequence diagrams, Gantt charts, class diagrams or ER diagrams inline, zoom into a detail and export a single diagram as PNG. A user who needs to distribute a document can export it to PDF with precise layouts, to HTML for the web, or to PNG for long documents, with typography and diagrams preserved. Someone who needs to make a small correction can switch into the CodeMirror-based editor, use Markdown shortcuts and search and replace, then return to reading. And because Windows, macOS and Linux builds are all available, the same reader can be used across different machines. Moji is aimed at people who read Markdown on a desktop and want it to behave like a document rather than source code: developers reading repository documentation, writers and note-takers working in Markdown, and anyone who deals with technical documentation that contains diagrams. It is explicitly free, open source under the MIT license, and requires no account. The site states that the source can be explored, development followed, issues reported, and the project's next chapter shaped through the public repository. Technically, the site names CodeMirror 6 as the editor foundation and Mermaid for diagram rendering. Moji runs on Windows, macOS and Linux, with distribution handled through GitHub Releases, and the interface is localised into six languages. Moji's primary value proposition is stated on its own homepage: opening Markdown should be as simple as opening a PDF. It delivers that by pairing a focused, lightweight desktop reader with the editing, diagram rendering and export tools needed when reading is not enough, and it does so free of charge, as open source, across Windows, macOS and Linux.
Devin Voice is a voice mode feature that allows you to talk naturally with Devin, Cognition's AI software engineer. It is designed for software developers and technical users who want to explore ideas, pressure-test approaches, and hand off work while away from their keyboard. Instead of typing, you can speak a task out loud, and Devin will plan, code, and deliver the work. The feature is accessed through the Devin web application, where you can start a voice call in Agent mode or within an existing session. This creates a hands-free way to interact with an AI software engineer, making it possible to continue development work even when you are not at your desk. The problem Devin Voice addresses is the friction of being tied to a keyboard when you need to delegate coding tasks or discuss technical ideas. Developers often have moments of inspiration or need to hand off work while away from their computer—whether commuting, walking, or simply taking a break. Typing is not always convenient or possible. Voice mode solves this by enabling natural spoken conversation with Devin. It matters because it removes the keyboard as a barrier to starting and managing development work. You can capture ideas as they come, verbally describe a task, and let Devin handle the implementation, all without sitting down to type. This makes the development process more fluid and accessible. One of the core features of Devin Voice is the ability to start a call easily. On the home page in Agent mode, or in an existing session, you click the voice call button beside the message box and allow microphone access. Any message you have already typed is sent when you start the call, so you do not lose your drafted context. This seamless transition from typing to talking ensures you can switch modes without interrupting your workflow. The voice call button is indicated by a waveform icon, and hovering over it shows the 'Start voice call' tooltip, making the entry point clear and discoverable. Once a call is active, you have several controls to manage the conversation. You can mute your microphone to pause your input, then unmute to speak again. If you are muted but need to say something quickly without toggling mute, you can hold the Space bar to talk, which is especially useful when you are not typing. You can also silence Devin, which turns off Devin's audio without muting your microphone, allowing you to continue speaking without hearing responses; clicking 'Unsilence Devin' restores the audio. To end the call, you simply click 'End voice call'. These controls give you fine-grained command over the voice interaction, making it adaptable to different situations. While a voice call is ongoing, you can navigate within Devin and the call stays connected. This means you can move between different views, check code, or review other parts of the application without ending the conversation. Your spoken conversation appears in the session history, so you can refer back to it later. The documentation also offers tips to make the most of voice mode. You are encouraged to interrupt Devin's work, ask questions, and clarify your thoughts as they occur. You can even interrupt Devin while it is talking, which keeps the conversation natural and dynamic. Additionally, if you have preferences for how Devin should speak—such as speaking faster or slower, or a particular communication style—you can simply ask, and Devin will adjust. The unique approach of Devin Voice lies in its integration with Devin's underlying AI capabilities. According to the product description, it is powered by GPT-Live for natural conversation, with Cognition's new SWE-2 coding model under the hood. This combination allows Devin to understand spoken tasks and translate them into planned, coded, and delivered work. The voice interface is not just a dictation tool; it is a full conversational interface with an AI software engineer that can act on your requests. This means you can speak a task and have Devin ship it, as the tagline says: 'You say it, Devin ships it.' The benefits for users are significant. You gain the ability to work hands-free, which is useful when you are away from your keyboard or occupied with other tasks. You can explore ideas and pressure-test approaches through natural conversation, which can be faster and more expressive than typing. Handing off work becomes as simple as speaking a task, and Devin takes care of the planning, coding, and delivery. The ability to interrupt and ask questions means you stay in control and can clarify details in real time. The session history provides a record of your conversations, and the speech preference adjustments let you customize the interaction to your liking. Overall, Devin Voice makes interacting with an AI software engineer more accessible and flexible. Concrete use cases for Devin Voice include exploring ideas when you are away from your desk, such as during a walk or commute. You can talk through a feature concept, and Devin can help refine it. Pressure-testing an approach is another scenario: you can verbally discuss trade-offs and edge cases, and Devin can respond with analysis or suggestions. Handing off work is a primary use case—speak a coding task out loud, and Devin will plan, code, and deliver it while you continue with other activities. You can also use voice mode to ask questions and clarify thoughts as they arise, interrupting Devin's work or speech to get immediate answers. Navigating within Devin during a call lets you review code or check other sessions without breaking the conversation. Finally, adjusting speech preferences allows you to tailor Devin's voice to your needs. Devin Voice is targeted at software developers, engineers, and technical teams who use Devin. It is particularly suited for those who want to hand off coding work or explore ideas while away from their keyboard. The feature is available within the Devin web application, and no additional integrations are mentioned in the documentation. The tech stack includes GPT-Live for conversation and SWE-2 for coding. Pricing and plan details are not specified in the provided content, but the product is part of the Devin platform, which can be tried at https://devin.ai. In summary, Devin Voice is a voice mode for Devin that lets you talk naturally with an AI software engineer to explore ideas, pressure-test approaches, and hand off work. With easy call initiation, flexible call controls, and the power of GPT-Live and SWE-2, it enables hands-free development and natural conversation. Whether you are away from your keyboard or simply prefer speaking over typing, Devin Voice makes it possible to ship work by speaking it out loud.
EasySpecs is a Spec Engineering Assistant and spec review platform built for teams practicing Spec-Driven Development. It documents undocumented codebases and turns them into trustworthy specifications, grounds AI agents in reality, and lets teams create Trust by Design Specs before code is written rather than after bugs pile up. The product is aimed at product owners, developers, technical product managers, and organization leaders who need product and engineering to share the same source of truth about an application. Its stated purpose is to close the gap between teams shipping at 1.5x and teams shipping at 100x — a gap EasySpecs describes as trust rather than speed. AI is accelerating how fast software changes. As EasySpecs frames it, AI agents generate code 100x faster than humans write it, but a team cannot review it all. The result is familiar to engineering leads and CTOs: “Agents generate faster than my team can review. We're drowning in AI PRs.” Developers describe the other side of the same problem: “I babysit the agent the whole run. If I look away, things go wrong.” Spec Driven Development is presented as the new standard, and with it come new consequences — a need for a tool for quality specs, new spec management requirements, documentation that lags, shared context that goes stale, overlap between product and engineering with no place to align together, and surging merge requests that create code review bottlenecks. EasySpecs positions itself as the response to each of those consequences. Step one of the EasySpecs workflow is understanding the code. EasySpecs produces functional documentation of your project with up to 98% LOC coverage assignment. That documentation is described as the first stone of Trust Engineering and as a foundation: functional documentation of the real system, so every later Spec starts from how the app actually behaves rather than from a guess. The stated benefit is that change requests start aware of real behavior and user intent, which lets teams make informed decisions instead of building on assumptions. Because the documentation reflects the actual application rather than someone's memory of it, it gives the rest of the workflow something concrete to build on — the factual baseline that intent and Specs are later grounded against. Without that baseline, every downstream step would inherit the same uncertainty the documentation exists to remove. Step two is polishing the intent and grounding it to the current codebase. When intent is fuzzy, EasySpecs helps you craft, clarify, and ground it to the current codebase before agents generate code, so that Spec-Driven Development has something trustworthy to drive. Intent is described as the ask behind the change — captured and grounded in how the app actually works, so product and engineering share one picture before Specs are written. The product surfaces this through a Specs accordion that shows Change, Intent, Diagram, and Spec steps. For technical product managers, the outcome is specs that developers can ship against, ready the moment engineering picks them up and integrated with Jira. This step matters because fuzzy stories burn engineering time: when the ask behind a change is unclear or divorced from the real system, developers end up interpreting rather than building. Step three is creating Trust by Design Specs. Once intent is clear, EasySpecs creates Trust by Design Specs with structured views and HTML-rendered views, so the change is visible and checkable before you write the code. Every Spec is sided by a Trust Spec. The Spec is the Spec-Driven Development Spec — what to build — presented in structured and HTML views the team can actually read. The Trust Spec holds validators, evals, and checks that sit beside the Spec, so you know how you will trust the change before agents generate code. The Product Hunt description also refers to reviewing specs including Oracles and Rubrics, and to developers working from Specs and Spec of Trust validators. The underlying principle is Trust by Design: define how you will trust the code before you write it, not after bugs pile up — and the sooner you set that bar, the less cost and fewer problems you carry. EasySpecs works in three steps and frames the whole loop as Trust Engineering: understand the code, polish the intent and ground it to the current codebase, then create Trust by Design Specs — before you ship, not after bugs pile up. Around that loop, EasySpecs acts as spec-driven change management. A dashboard shows change requests and linked Spec status across projects, so every change request and its Spec can be tracked. Documentation auto-syncs, addressing the problem of docs that lag and shared context that goes stale. EasySpecs also presents itself as one Spec-Driven operating system for tech and product, introducing Spec-Driven Development across a team so product and engineering share the same source of truth. The stated aim is that the gap between teams operating at 1.5x and teams operating at 100x is not speed but trust, and that grounding agents in reality lets you scale agentic development without babysitting, documenting the foundation once. EasySpecs states a set of outcomes tied to each consequence of faster shipping. Where a tool for quality specs is needed, EasySpecs creates quality specs easily. Where shipping faster demands new spec management, EasySpecs provides spec-driven change management. Where docs lag and shared context goes stale, EasySpecs auto-syncs documentation. Where product and engineering overlap with no place to align, EasySpecs offers one place to align together. Where merge requests surge and code review bottlenecks form, Trust Engineering eases merge requests, making spec review the new merge request review. For developers, the promise is concrete: stop babysitting the agent, and start generation from clear intent and checks rather than vibes. For product owners, change requests can be grounded in the real app and shaped into Trust by Design Specs the team can actually see. One testimonial sums it up: “My team finally speaks the same language about specs. The pace of change was so fast we could not align. Now with EasySpecs, all clear.” Concrete scenarios appear throughout the content. A team working with an undocumented codebase can have EasySpecs produce functional documentation first, so change requests start aware of real behavior. A developer struggling with AI-generated pull requests can move review upstream to Specs and Spec of Trust validators instead of reviewing an endless stream of code. A technical product manager can write a spec against the system that developers can ship against, ready the moment engineering picks it up through Jira. A product owner can ground a change request in the real application, polish the intent behind it, and shape Trust by Design Specs the team can see. An organization leader can introduce Spec-Driven Development across a team so product and engineering work from the same source of truth, with a dashboard tracking every change request and its linked Spec across projects. And a developer working in an editor can stay inside VS Code, Cursor, Antigravity, or any VS Code-compatible IDE while the workflow runs. EasySpecs is built for product owners and developers — two sides of the same Trust by Design loop — and also speaks directly to technical product managers and organization leaders. The workflow is integrated with Jira and Linear, and with VS Code, Cursor, Antigravity, and any VS Code-compatible IDE on the development side. The product is listed on Product Hunt under SaaS, Developer Tools, and Development, and the site includes a section on agentic coding noting Loop Engineering, Graph Engineering, Context Management, and more. No pricing or plan details are stated in the available content. EasySpecs positions Trust Engineering at the center of AI-accelerated software delivery: document the real system once, polish intent against that reality, then define how you will trust the change before agents generate code. By turning Specs — including their validators, evals, checks, and the review of Oracles and Rubrics — into something reviewable, it reframes spec review as the new merge request review and gives product and engineering one shared source of truth. The takeaway is straightforward: the gap between 1.5x and 100x is not speed, it is trust, and EasySpecs is built to supply it.
Raycast 2.0 is the next generation of Raycast, described by its creators as a new foundation, redesigned from the inside out. It is a launcher for macOS that acts as your shortcut to everything, and this release adds AI that can take action across your apps, Automations for recurring tasks, and Projects to keep ongoing work together. It is built for Mac users who want to move quickly across their desktop, combining a command launcher, file search, dictation, and an AI chat experience in one place. Raycast 2.0 is available to download now, and it requires macOS Tahoe and Apple Silicon. The update exists because Raycast chose to rebuild its launcher on a new foundation rather than continue patching the previous one. Raycast V1 was already described as the best Launcher, so the bar for 2.0 was high: the goal was to keep what people relied on while modernizing the interface, the hotkey handling, and the AI experience. That scope comes with trade-offs the team states openly. Raycast calls this a major update and tells users to expect frequent updates and occasional rough edges. On installation, the new Raycast will replace Raycast V1; there are just a handful of missing features, and those will be added soon. The most concrete risk of any rebuild is losing a carefully tuned setup, so 2.0 is designed to bring your configuration with you instead of asking you to start over from scratch. The core of the new release is a reworked AI experience built around two surfaces. Quick AI keeps you in the same Tab while adding more power, so you do not have to leave what you are doing to get an answer. AI Chat collects skills, agents, and memory in one place, giving AI work a persistent home inside the launcher rather than a one-off query box. Beyond answering questions, the AI in Raycast 2.0 can take action across your apps, which means the assistant is not limited to conversation alone. You can also connect your own ChatGPT or Claude account and put AI to work alongside the commands and extensions you use every day. Connecting your own account lets the AI you already use become part of the same surface as your launcher commands and extensions. Several changes target the everyday speed of the launcher itself. File Search now sits in Root, described as one less step to find your files, so files are reachable from the top level of your search instead of requiring extra navigation. File search is also faster in v2, and the FAQ lists quicklinks and snippets tagging among the additions in this release. Because root search is where queries begin, moving File Search there shortens the path between thinking of a file and opening it. Quicklinks and snippet tagging build on the same idea: your own shortcuts, snippets, and saved searches stay close at hand rather than buried inside menus, which matters for people who run the same workflows dozens of times a day. The look and feel of Raycast has been updated to feel right at home on macOS Tahoe, giving the v2 interface a refreshed appearance. The release also adds built-in dictation under the banner Type with your voice, so text can be entered by speaking without leaving the launcher. Settings have been reorganized, making the growing set of options easier to navigate as the product adds capabilities. In addition, you can configure inline, meaning hotkeys and aliases can be assigned directly from root search. That combination matters because a launcher is only fast when it is configured the way you actually work, and v2 places configuration and voice input on the same surface as search. Raycast 2.0 also treats the upgrade itself as a migration rather than a fresh start. During onboarding you are prompted to import or migrate your data from Raycast v1, and if you skip the step or want to rerun it later, you can use the documented commands. The Migrate from Raycast v1 command automatically migrates your data, and importing settings at this step also imports and migrates all of your shortcuts to Raycast v2 and disables them from working in Raycast v1, ensuring that your hotkeys do not conflict. Raycast calls this the recommended approach because some extra data is not imported with the manual route, including Clipboard History, Wrapped, and the Emoji picker. The alternative, Import Settings and Data, is a manual import from a .rayconfig file that you must export from Raycast v1. For people who build their own tools, custom extensions also need attention: to import correctly you must be on the latest version of Raycast V1, or at least v1.104.16, and if they did not import automatically you can run npx @raycast/api@latest dev, which is designed to pick up the new version if it is running. The benefits of Raycast 2.0 come from combining a faster launcher with AI that can act. Files are reachable in fewer steps, AI answers arrive in the same Tab through Quick AI, and longer AI work has a dedicated place in AI Chat with skills, agents, and memory. Built-in dictation offers a different way to input text when typing is slower than speaking. Because hotkeys and aliases can be assigned from root search and your existing shortcuts migrate from v1, the muscle memory you have already built keeps working in the new version. The result is a launcher that feels familiar on day one while offering more capability than before, which is exactly the balance the team set out to strike with a major rebuild. In practice, that shows up in concrete workflows. You can type a file name and open the result directly from root search instead of navigating to a separate file search view. You can ask Quick AI a question in the same Tab while staying in the flow of your current task, or move into AI Chat when a task needs skills, agents, and memory to be sustained over time. Ongoing work can be kept together in Projects, while Automations handle recurring tasks so they do not have to be triggered by hand. When writing is faster by voice, built-in dictation takes over, and when you want a command at your fingertips you can assign a hotkey or alias to it straight from root search. Upgrading is itself a workflow: import during onboarding, or run the migration commands afterwards. Raycast 2.0 is aimed at Mac users, and specifically at anyone running macOS Tahoe on Apple Silicon, since those are the stated minimum requirements. People already using Raycast V1 are the primary audience for the upgrade, and that includes extension developers, who need to be on Raycast V1 v1.104.16 or later for custom extensions to import correctly, or run the Raycast API dev command if they did not. Alongside the free download, Raycast offers Pro, Teams, and Enterprise plans, with pricing published on the Raycast site, plus an iOS app, a Windows page, and a browser extension in the wider Raycast family of products. AI in v2 can be used with your own ChatGPT or Claude account, letting you bring an existing AI subscription into the launcher. Taken together, Raycast 2.0 is a rebuild of a launcher that many Mac users already depend on, and the site sums it up directly: the launcher, relaunched. A new foundation, redesigned from the inside out, with AI that can take action across your apps, Automations for recurring tasks, Projects that keep ongoing work together, AI Chat with skills, agents, and memory, built-in dictation, faster file search in root, and a migration path that carries your settings, shortcuts, and extensions forward. The value proposition is the combination: more capability in the same fast surface you already know, without asking you to rebuild your setup from zero.
Loqua is context-aware voice typing and dictation software built for Mac and Windows. It converts natural speech into polished, ready-to-use writing, understands what is on your screen, offers read-aloud when you would rather listen than read, and lets you trigger everyday actions by voice. Its stated purpose is to help people move from thoughts to being done — less typing, less context switching, and more time in flow. Loqua is aimed at anyone who would rather think than type: professionals who write all day, developers and engineers, product managers, designers, marketers, founders, writers, researchers and students. The keyboard has long been the bottleneck between having an idea and getting it written down. Traditional keyboard typing runs at roughly 45 words per minute, while Loqua's dictation runs at 220 words per minute — a difference the company presents as saving up to 3 hours per day. Raw speech, however, is rarely usable as written text: it is full of filler words, repetition and half-finished sentences. Loqua removes filler words, cuts repetition and refines phrasing in real time so that what lands on screen is ready to send. A second problem Loqua targets is context switching: stopping work to open another app, look something up, translate it or rewrite it breaks concentration. Loqua aims to keep people inside the app they are already working in. The core of Loqua is dictation with real-time cleanup. When you speak, Loqua strips out filler words, trims repetition and refines your phrasing, so the sentence that appears on screen reads as if it had been carefully written rather than spoken. This happens in real time, which means you do not have to go back and re-read everything you just said. Loqua also recognises structure in speech and builds it automatically: if you think in bullets but speak in blocks, Loqua derives lists, headings and hierarchy on its own, so you do not have to dictate formatting out loud. For anyone who writes long documents, meeting notes or structured updates, this removes the tedious formatting pass that normally follows dictation. Capture to Ask addresses the moments when the answer is on your screen but you cannot figure it out. Using a shortcut, you select any part of the screen — a table, a chart or anything else — then speak your question. Loqua returns an answer, an analysis, a translation or a summary without you having to switch apps. It is a three-step flow the company describes as Capture, Ask, Know. Related to this is Ask and Edit: highlight anything, whether it is a product description, a draft or a note, speak your instruction, and Loqua rewrites it on the spot. There is also a simpler ask-anything path — hit a shortcut, ask a question out loud, and get an instant answer without leaving your current app, which is useful when you are stuck mid-task. Translation lets you speak in your own language and deliver in someone else's, with native phrasing in nearly 100 languages, returned instantly. That makes it practical for international teams, multilingual correspondence and anyone writing in a second language. Command to Go turns your voice into a hands-free command hub: with a shortcut you can set reminders, open apps, search routes, place calls and send texts, so you can manage small tasks without jumping between applications. AI Podcast read-aloud works in the opposite direction — select text and Loqua reads it aloud, which the company suggests for morning news and multitasking, effectively giving you a hands-free text-to-speech assistant. Loqua's approach is to sit globally on top of your existing workflow rather than replace it. You invoke it with one shortcut in any text field, so it works in tools like Terminal, Slack, Notion, email, Google Docs, Microsoft Word, VS Code, Teams, Figma, Obsidian, GitHub and many more, with your voice landing right where your cursor is. The company describes the experience as no switching and no waiting, with a zero-latency feel that makes the tool easy to forget about. Loqua is built by a dedicated voice AI team with full model iteration capabilities, and the roadmap includes meeting transcription, multimodal capabilities and a Skill Market. Users report that Loqua adapts tone automatically — shifting register between, for example, a message to a CEO and a Slack message to a team — by understanding the context it is typing into. The benefits the company and its users describe are time and flow. Because dictation runs at 220 words per minute rather than 45, and because cleanup and formatting happen automatically, people report writing that used to take an hour finishing in minutes — one user summarises 30-minute writing sessions becoming 5-minute speaking sessions, and another says PRDs written by voice save at least an hour a day. Beyond speed, Loqua reduces the proofreading burden: users report they have stopped going back to check output, even when dictating framework names, library names and acronyms. For people with repetitive strain injury, Loqua is described as making work possible again rather than merely faster. Non-native English speakers say it makes their writing sound native, and international users say translation is instant and natural. Concrete use cases appear throughout the site. Product managers write PRDs, standup notes, sprint recaps and stakeholder updates by speaking. Engineers dictate coding notes, documentation and terminal input, and use it in Slack, Notion and their IDE. Designers keep their hands on Figma and narrate case studies and design processes. Content strategists and marketers draft LinkedIn posts, email campaigns, blog outlines and ad copy by voice and then polish from there. Founders and executives who context-switch all day speak thoughts into whatever app is open, and heavy email users cut correspondence time. Consultants talk through client debriefs to get clean summaries, UX researchers dictate research notes between interviews, and PhD candidates use the cleanup and formatting for academic writing. Sales teams use translation to send emails in a different language from the one they speak. Loqua runs on Mac and Windows as a desktop application you download from the site, with a 14-day free trial. It advertises support for a very broad set of applications — among them Google Docs, Word, Notion, Slack, Gmail, Figma, VS Code, Teams, Obsidian, Google Sheets, Zoom, Excel, GitHub, Terminal, Discord, Outlook, IntelliJ IDEA, PowerPoint, GitLab, WhatsApp, Telegram, Stack Overflow, OneNote, Canva, Photoshop, LinkedIn, Google Slides, X, Sketch, Reddit, Illustrator, Confluence, Evernote, Facebook, Adobe XD and Google Keep. There is also a developer programme: maintainers of public open-source projects can apply for a Developer Grant giving free access. The site states that thousands of professionals use Loqua every day. Loqua's core promise is that your thoughts should not have to slow down for a keyboard. By combining high-speed context-aware dictation, automatic cleanup and structure, screen understanding, translation, voice editing and voice-triggered actions in one shortcut-driven tool, it turns rough ideas into ready-to-use writing and keeps work moving inside the apps you already use.