[{"data":1,"prerenderedAt":516},["ShallowReactive",2],{"blog-list-en-US-featured-1":3},{"list":4,"total":513,"page":514,"page_size":515},[5,26,40,51,66,76,104,128,152,176,200,211,221,231,268,311,350,377,416,447,483,494,503],{"id":6,"slug":7,"title":8,"seo_title":9,"seo_description":10,"summary":11,"cover_alt":12,"author":13,"category":14,"tags":15,"cover_url":19,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":24,"modified_at":24,"content":25},"1791417600000-musework-tablebench-table-analysis-ranking","musework-tablebench-table-analysis-ranking","MuseWork's table analysis ranks first on TableBench's Methodology leaderboard","MuseWork Tops TableBench's Methodology Leaderboard for Table Analysis","MUSE PULSE, the table-analysis capability in MuseWork, ranks #1 on TableBench's Methodology leaderboard with 80.64. See what was tested and try a better question on your own data.","What MuseWork's TableBench result tells us about turning spreadsheet data into answers a team can use.","MUSE PULSE ranks first on TableBench's Methodology leaderboard with an overall score of 80.64 and scores for fact checking, numerical reasoning, data analysis, and visualization.","MuseWork Content Team","Product",[16,17,18],"TableBench","MUSE PULSE","MuseWork","/images/musework-tablebench-complete-cover.webp",2048,1152,false,true,1791417600,"A spreadsheet can show that revenue changed. It takes more work to tell your team where the change came from: which weeks are being compared, whether returns count, and which region moved the total. TableBench tests the reasoning behind questions like these.\n\nOn **October 8, 2026**, **MUSE PULSE**, the table-analysis capability available to MuseWork users, ranked **#1 on [TableBench's Methodology leaderboard](https://tablebench.github.io/)** with an **overall score of 80.64**. Here's what the benchmark asks, what stands out in our results, and how to ask better questions of your own data.\n\n![TableBench Methodology top 10 on October 8, 2026: MUSE PULSE ranks first with an overall score of 80.64, alongside category scores and a human-performance reference.](/images/musework-tablebench-methodology-top-10.webp)\n\n## Why this is a demanding test of table questions\n\nTableBench goes beyond looking up a value in one cell. Researchers assembled **886 test cases across 18 types of question**, grouped under fact checking, numerical reasoning, data analysis, and visualization. According to the paper, the questions require **6.26 reasoning steps on average**—a measure of their complexity.\n\nThe researchers designed it to get closer to the kind of table questions that arise at work, where an answer may require checking several entries or calculating an intermediate figure. The official site provides the paper, test data, evaluation code, and **two separate leaderboards: Methodology and Large Language Models**. Our result appears on **Methodology**; the two tracks should be read by their own labels. The project also updated its test set and refined several scoring metrics in April 2025, so the current leaderboard—not a score quoted from the original paper—is the place to verify today's ranking.\n\nThe differences between the question types are useful in everyday work:\n\n- **Fact checking:** A note says a product's sales rose in every quarter. Does the table actually support that claim across the relevant periods?\n- **Numerical reasoning:** The answer is not printed in one cell. You must select the right rows, aggregate them, and calculate a comparison or rate.\n- **Data analysis:** A team needs to describe a pattern, spot an unusual change, or assess a trend. That takes a clearer question than “analyze this spreadsheet.”\n- **Visualization:** The finding needs a chart that communicates the comparison. The benchmark's chart task checks whether a correct chart is generated on the first attempt.\n\nThe official evaluation guide scores fact checking and numerical reasoning by **exact match**. Data-analysis questions use different metrics depending on the task: an impact question is checked differently from a forecast or an open-ended explanation. Visualization uses **Pass@1**, asking whether the chart is right on the first attempt. That's why the category breakdown tells you more than the overall score alone.\n\n## What MUSE PULSE scored—and what to read into it\n\n| TableBench category | MUSE PULSE score | A work question it resembles |\n| --- | ---: | --- |\n| Fact checking | **91.67** | Is the statement in the update supported by the table? |\n| Numerical reasoning | **93.70** | What is the change after the correct rows and periods are counted? |\n| Data analysis | **74.04** | Which pattern deserves attention, and how should it be described? |\n| Visualization | **88.0** | Can the result be communicated in a chart? |\n| **Overall** | **80.64** | **Reported aggregate on the Methodology leaderboard** |\n\nThe fact-checking and numerical-reasoning scores are especially relevant to weekly updates and business reviews: if the figures or the comparison are wrong, even a well-written explanation falls apart. Data analysis scores lower than the other three areas, showing us where there's more to improve. TableBench also lists a **human-performance reference score of 85.91**. The #1 ranking is for table questions on its Methodology leaderboard.\n\n## Turn one spreadsheet question into a piece of work\n\nSay you're preparing a Friday sales update from a table of weeks, regions, product lines, revenue, and returns. Rather than asking for a general analysis, tell MuseWork what your manager needs to know:\n\n> “Using this weekly sales table, compare the latest complete week with the week before it. Show which region and product line contributed most to the change in net sales. State whether returns are included, show the figures behind the comparison, and write three points for a leadership update. Suggest a chart only if it makes the change easier to understand.”\n\nWhat makes the request useful is its specificity: **time period, metric, breakdown, calculation rules, supporting figures, and audience**. Check the selected rows, the definition of net sales, and the calculations against your source. Then keep going in the same task—tighten the explanation, look at a different segment, or prepare a version for another audience.\n\nThat's the work MuseWork is built to help with as **your professional AI agent**: not just finding a number, but turning the materials you already have into a result you can review, share, and build on.\n\n**Have a spreadsheet open? [Start with one question in MuseWork](https://museai.im/en-US/chat?scene=musework-tablebench-table-analysis-ranking).** The comparison you need for your next update is a good place to begin.\n\n## Prefer to work with files on your computer?\n\nIf the files are on your computer, [download MuseWork Desktop](https://museai.im/en-US/download) for macOS, Windows, or Linux. With your permission, you can bring the local files you choose into the task. Prefer a browser? You can start on the web too.\n\n### Sources\n\n- [TableBench leaderboard and benchmark overview](https://tablebench.github.io/)\n- [TableBench paper: task design and dataset statistics](https://arxiv.org/abs/2408.09174)\n- [TableBench evaluation metrics](https://github.com/TableBench/TableBench#-evaluation-metrics)",{"id":27,"slug":28,"title":29,"seo_title":30,"seo_description":31,"summary":31,"cover_alt":32,"author":13,"category":33,"tags":34,"cover_url":36,"cover_width":20,"cover_height":37,"is_pinned":22,"is_featured":23,"published_at":38,"modified_at":38,"content":39},"1790726400000-openai-dots-work-guide","openai-dots-work-guide","OpenAI Dots for Work: What to Delegate, What to Review, and How to Get Started","OpenAI Dots for Work: Uses, Setup, Permissions | MuseWork","What can OpenAI Dots do at work? Learn who can use them, how to create one, where approvals matter, and how to frame a useful assignment.","A pink Dot character beside the OpenAI dots title on a minimal editorial cover.","AI News",[35],"OpenAI Dots","/images/openai-dots-blog-cover.webp",1238,1790726400,"OpenAI introduced dots on September 29, 2026, as always-on agents that can continue working between conversations. A dot has its own cloud computer, can use apps you choose to connect, and can bring work back for your review. That sounds different from opening a chat, asking one question, and starting over next time. But a useful first question is more specific: **What responsibility would you give a dot, and what would you still want to approve yourself?**\n\nHere is what OpenAI says dots can do, who can access them at launch, and how to frame a real work assignment. We'll use a product-launch example to make the boundary between ongoing monitoring and a finished decision brief concrete.\n\n## What is an OpenAI dot?\n\nA dot is a personal agent in ChatGPT, powered by GPT-6 Astra. According to OpenAI's launch announcement, it has a cloud computer and browser, can work with connected apps, and can carry context across tasks. You can message or call it in ChatGPT on desktop and web, and in the mobile app when access is available for your account after setup; OpenAI also describes messaging through Slack and Microsoft Teams.\n\nThe distinction is continuity. OpenAI describes a dot that can work on several projects, update you when it needs a decision, and learn from your feedback. In one of its launch examples, a product-launch dot revises draft materials as scope changes. In another, it looks at new evidence and updates an analysis for human review. Those are OpenAI's examples of intended use, not a promise that every account has every integration or that a dot's output is automatically correct.\n\nA dot's personal role is also different from the **specialist dots** OpenAI previewed for organizations. Specialist dots with dedicated responsibilities are starting in focused enterprise pilots; they are not the default self-service dot you create today.\n\n## Who can use dots, and where do you create one?\n\nOpenAI says it is **rolling out** dots to **Pro and Business Premium** users, while Enterprise workspaces can try the beta when an admin enables it. The feature may not have reached every eligible account yet. The first dot is included with a Pro or Business Premium plan at no extra charge; deeper work still has a plan allowance.\n\nTo create the first dot, use the **ChatGPT desktop app or desktop browser**, follow the setup prompts, name it, and review any app connections and their permissions. After initial setup, OpenAI says you can message it in the ChatGPT mobile app when mobile access is available; you cannot currently create one on mobile, and dots are not supported on mobile web. Its getting-started guide explains how to inspect activity, review scheduled work, add custom rules, and pause the dot. If you do not see the option, check your account's eligibility and rollout status before assuming your device is the problem.\n\n## What can a dot do without asking you?\n\nThat depends on the connected app, its permissions, the action, your rules, and any workspace policy. OpenAI says you choose which apps to connect. It describes built-in rules and **Custom Rules** that can allow a supported action, require approval, or block it. You can inspect progress and redirect the work. Your own computer stays separate from the dot's cloud computer unless you explicitly connect it.\n\nThere is an important difference between a dot working in the background and taking an outward action. OpenAI calls the former **proactive research**: it can use read-only tools in apps you have connected to look for useful information, but that mode cannot directly message someone or change app content. Other tasks follow the app permissions and review rules you set. Decide which changes you want to check before they happen, and verify consequential results. OpenAI describes these controls in its Help Center.\n\nFor a product manager, that suggests a practical division: let the dot surface a change in customer feedback or a newly available document, but review the evidence, proposed wording, recipient, and commitments before anything consequential is sent or published. OpenAI itself cautions that dots can make mistakes. An approval prompt is a control, not a substitute for checking the work.\n\n## A work example: track a launch, then prepare a decision\n\nSuppose your team is preparing a release. Feedback is coming through an approved support source, the product spec is changing, and leadership wants to know whether to adjust the launch message. OpenAI's own launch example describes a dot learning a team's audience and standards, then revising launch materials when scope changes. To turn that broad scenario into an assignment someone can supervise, define five things up front:\n\n1. **Goal:** Flag changes that may alter the launch claim or timing; prepare a proposed update rather than publish it.\n2. **Sources:** Name the specific connected feedback and product sources the dot is allowed to use. Do not assume access to every app simply because OpenAI's plugin ecosystem supports many apps.\n3. **Trigger:** Explain what counts as a material change—for example, a confirmed feature removal or repeated customer confusion about a promised capability.\n4. **Review boundary:** Ask for source links, the proposed change, unresolved questions, and your approval before outward-facing messages or edits.\n5. **Done criterion:** A short change brief that says what changed, who is affected, what needs a decision, and what remains unverified.\n\nA starting instruction might read:\n\n> Watch the launch information I've authorized you to access. When a confirmed scope change or repeated customer issue affects our release claims, prepare a brief with the original claim, the new evidence, affected materials, and a recommended revision. Cite the source of each important claim. Do not send a customer message or publish a change without my review.\n\nTreat this as an **illustrative assignment**, not a tested dot recipe or a guarantee that a particular app supports every step. Check the available connection and permission controls in your own account. As the work comes back, verify whether the feedback is representative, whether the spec change is final, and who owns the launch decision. Those are judgment calls a polished draft cannot settle on its own.\n\n## Make the finding useful to the launch team\n\nThe change brief is a starting point, not the launch decision. A product manager still needs to check the approved spec, see whether the customer feedback reflects a wider issue, and identify which claims and materials need review. The useful question is: what can the team say with confidence now, and what is still unresolved?\n\nIf you need to turn those materials into a reviewable recommendation, you can make that a separate, scoped task in MuseWork. Give it the approved spec and feedback you are permitted to share, along with any relevant public release information, and name the people who will review the result. [MuseWork Deep Research](https://museai.im/en-US/features/deep-research) can work from your files and public sources to organize evidence, flag gaps, and produce a structured report. A request could be:\n\n> Using the approved spec, customer feedback, and public release information I provide, check which launch claims still have support. Show the source for each finding, separate confirmed changes from open questions, and recommend which materials the product and marketing leads should review before publication.\n\nThe team can then check the report against the sources and decide what to change. If that is the result you need, [start a research task in MuseWork](https://museai.im/en-US/chat?scene=deep-research) with the materials you choose to provide. It is one way to move the launch work forward, not a required step in using dots.\n\n## Before you delegate, decide what “done” means\n\nThe strongest first assignment for a dot is not “handle the launch.” Give it approved sources, a trigger, a review point, and a concrete output. When the work becomes a decision, ask for the evidence, open questions, and a proposed next step in a form a person can actually review. That is how continuous attention becomes work the team can move forward.\n\n## References\n\n1. [OpenAI: Introducing dots](https://openai.com/index/introducing-dots/)\n2. [OpenAI Help Center: Getting started with your dot](https://help.openai.com/en/articles/20001530-getting-started-with-your-dot)\n3. [OpenAI Help Center: Dots privacy, security, and safety FAQs](https://help.openai.com/en/articles/20001529-dots-privacy-security-and-safety-faqs)",{"id":41,"slug":42,"title":43,"seo_title":44,"seo_description":45,"summary":45,"cover_alt":46,"author":13,"category":33,"tags":47,"cover_url":49,"cover_width":20,"cover_height":37,"is_pinned":22,"is_featured":23,"published_at":38,"modified_at":38,"content":50},"1790726400000-claude-sonnet-5-5-whats-new-work","claude-sonnet-5-5-whats-new-work","Claude Sonnet 5.5: What's New and How to Use It for Work","Claude Sonnet 5.5: What's New and How to Use It at Work","What changed in Claude Sonnet 5.5? Explore its speed, work use cases, availability, and a practical report-to-slides example grounded in source material.","Centered Claude logo with its orange spark symbol and Sonnet 5.5 caption on a minimal editorial cover.",[48],"Claude Sonnet 5.5","/images/claude-sonnet-5-5-centered-cover.webp","[Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) arrived on September 28, 2026. Anthropic positions it as a faster, more efficient upgrade to Sonnet 5 for well-defined work: coding, writing, charts, documents, slides, and spreadsheets. If you're wondering whether the update matters for your job, start with what actually changed—then try it on a task whose source material and expected result you can check.\n\n## What's new for everyday work?\n\nAnthropic says Sonnet 5.5 generates output more than 30% faster than Sonnet 5. In its tests, the model also used fewer tokens and cost up to 30% less per task. Its API list price, however, remains $2 per million input tokens and $10 per million output tokens. These are model-generation and API-usage figures, not a measure of how long it takes a team to finish a report.\n\nAnthropic also tested a workflow much closer to the one above: it supplied a public company's quarterly earnings materials, earnings-call transcripts, and a slide template, then asked for a ten-slide operating review. Two experts judged the first draft ready to send. That's one internal example, but it shows how much the quality of the brief and source material matters.\n\nBeyond that example, Anthropic reports clearer writing, stronger chart understanding, and better adherence to slide templates. It also reports big gains in coding evaluations, though a coding benchmark doesn't tell you how accurate your financial deck will be. Sonnet 5.5 is available in Claude's web and mobile apps and on developer platforms. Anthropic still positions Opus 5.5 above it for complex, open-ended work that requires sustained judgment.\n\nFor a first try, choose a task with a clear question and files you know well. A monthly review is a good example: you can check whether the draft explains the figures or merely sounds convincing.\n\n## A small example: what did the campaign change?\n\nTake these hypothetical month-end figures:\n\n| Metric | Last month | This month |\n|---|---:|---:|\n| Revenue | $120,000 | $138,000 |\n| Orders | 2,000 | 2,300 |\n| Average order value | $60 | $60 |\n| Ad spend | $20,000 | $25,000 |\n\nRevenue and orders are both up 15%; average order value is flat. Ad spend is up 25%. That's enough to say spending increased faster than sales. It isn't enough to credit the campaign with all the growth, or to say profit improved. We'd need attribution and margin data for those claims.\n\nBefore prompting, gather the two monthly exports, campaign notes, metric definitions, and the actual question leadership must decide. Check whether “revenue” means the same thing in both files. A report based on payment date and a spreadsheet based on order date may disagree without either being wrong.\n\nThen ask for a draft that keeps the uncertainty visible:\n\n> Using these two monthly exports and campaign notes, write a one-page operating review for leadership. Start with three findings supported by the files. For each important number, name the source and reporting period. Separate documented explanations from possible causes. Close with the decision about next month's ad budget and the information still needed. Do not treat revenue growth as profit growth.\n\nWhen the draft comes back, trace the three most important numbers to their original rows. Check that it hasn't turned “the campaign ran during the increase” into “the campaign caused the increase.”\n\n## Take the approved story into the meeting\n\nOnce the report has been reviewed, the presentation has a job: make the conclusion and decision easy to follow, not just divide the report across slides.\n\n> Turn the approved review into six leadership slides: the headline, revenue and orders, ad spend, what the evidence explains, decision options, and next steps. Give each slide a conclusion-led title, one main chart or table, and brief speaker notes. Keep the dates and definitions visible. Use our template if provided; flag missing evidence rather than filling it in.\n\n“Revenue rose 15%, while average order value stayed flat” is a better title than “Monthly performance.” Don't add a profit chart without profit data. If the team needs to edit the deck, ask for an editable file and open the actual export before sending it around.\n\nThe same goes for the workbook. If next month's numbers will be added, ask for separate source-data, calculation, and summary sheets. Preserve the original values, use formulas for month-over-month changes, and label missing numbers differently from zeros. Check the export options in the Claude product you're using, then open the finished file to make sure the formulas and editing features work as expected.\n\n## When is Sonnet 5.5 a good fit?\n\nA recurring report with known inputs, audience, and output is close to Anthropic's description of a *well-scoped everyday task*. A broad strategy question with conflicting evidence and no settled decision is different; Anthropic says Opus 5.5 remains stronger for complex, open-ended work requiring sustained judgment.\n\nFor a report about a recent event, bring current sources: Anthropic's [model specifications](https://platform.claude.com/docs/en/models/overview) list June 2026 as Sonnet 5.5's reliable knowledge cutoff.\n\nThe model is one part of the job; gathering the right project context and carrying a reviewed report into a deck is another. If that is the part you want help with, MuseWork can use relevant material from work sources you authorize, such as Slack or Google Drive, to draft a report and then build a [presentation](https://museai.im/en-US/usecase/research-to-presentation) from the findings you approve. Give it the timeframe, audience, and decision you need to support. For information beyond your own records, [Deep Research](https://museai.im/en-US/features/deep-research) is another place to start.\n\n[Start a report in MuseWork](https://museai.im/en-US/chat) · [Explore MuseWork apps](https://museai.im/en-US/download)\n\n**Sources:** [Anthropic's announcement](https://www.anthropic.com/claude-sonnet-5-5); [Claude Sonnet availability](https://www.anthropic.com/claude/sonnet); [model specifications](https://platform.claude.com/docs/en/models/overview); [API pricing](https://platform.claude.com/docs/en/about-claude/pricing).",{"id":52,"slug":53,"title":54,"seo_title":55,"seo_description":56,"summary":56,"cover_alt":57,"author":13,"category":58,"tags":59,"content":61,"cover_url":62,"cover_width":63,"cover_height":64,"is_pinned":22,"is_featured":23,"published_at":65,"modified_at":65},"1790640000000-pdf-report-to-presentation-guide","pdf-report-to-presentation-guide","How to Turn a PDF Report into a Presentation for a Meeting","How to Turn a PDF Report into a Meeting Presentation | MuseWork","Have a long PDF and a short meeting? Learn how to turn a report into clear, editable slides without copying every page or losing the important details.","Minimal graphic reading PDF and PRESENTATION, emphasizing a report reshaped for an audience.","Guides",[60,18],"Presentations","It's late afternoon when your product lead forwards a 30-page PDF of customer interview findings: 'Can you walk the team through this at tomorrow's product review?' The research is finished; the meeting deck isn't. You have one evening to decide what belongs on screen and what can stay in the report.\n\nSuppose the meeting needs to decide whether to test a shorter onboarding flow. Your teammates don't need all 30 pages pasted into PowerPoint. They need to see where interviewees stopped, what the report actually supports, and which small change the team should try next. That's a different job from converting the PDF's pages into slides.\n\n## Decide what people need from this meeting\n\nBefore opening a slide template, finish this sentence: “By the end of the meeting, we need to decide whether to ___.” If the answer is “test a new onboarding flow,” keep the parts of the PDF that help people make that call. Move the interview method and extra charts to an appendix, where you can pull them up if someone asks.\n\nA simple slide order is: the question, your answer, the strongest findings, what to try, and what you still don't know. You don't have to follow the PDF's table of contents.\n\n## Make each slide title say something useful\n\n“Survey findings” makes the audience wait for the point. “Customers leave before finishing setup” gives them one. Put the chart or quote beneath that title. Check its date, sample size, and labels against the original: a percentage without its original context can be misleading.\n\nTry reading only the slide titles aloud. If they tell the story on their own, your deck is probably easier to follow. If they sound like a list of report chapters, rewrite them before polishing the design.\n\n## See the report become a meeting outline\n\nFor example, suppose the PDF says interviewees stopped during setup, but the report has not established why. Do not put “Onboarding is broken” on a slide. Draft “Test a shorter setup before changing the entire flow,” followed by the observed friction and the report's actual sample and date. Put the interview method and alternative explanations in the appendix. That gives the room something to decide without pretending the report proved a cause.\n\n## Make the first version in MuseWork\n\nOpen [PDF to Presentation](https://museai.im/en-US/usecase/pdf-to-presentation) and upload the PDF, or, with your permission, use the report from a connected service such as Google Drive or OneDrive. If you also authorize relevant project notes or discussions, they can provide context around why the report matters, so you spend less time retelling the backstory. Still tell MuseWork who will be in the room and what decision the deck should support. With MuseWork's official **Create Slides** skill, choose a template and the output that fits your job: **PowerPoint** for an editable deck, **Online presentation** for a presentation you can view and share on the web, or **Image presentation** when you want a more visual, page-by-page deck. If you plan to keep editing the slides, choose PowerPoint. Then pick a slide-count **range**; the menu offers 4–8, 8–12, 12–16, 16–20, and 20–24 slides, not an exact count.\n\nYou can start with this request:\n\n> Make a PowerPoint draft from this PDF for a product review. The team needs to decide whether to test a new onboarding flow. Start with the main finding, show the two or three pieces of evidence that matter most, and finish with a proposed test. Put the detailed method in the appendix. Use the 8–12-slide range. Mark any figures I should check in the report.\n\nMuse Slides helps turn the material into an editable first version. Before sharing it, compare every important number, quote, and chart with the PDF. Check that a finding hasn't turned into a stronger claim than the source supports. Then make sure the last slide tells the team what you're asking them to do.\n\nA good deck does more than shorten the report. It gives everyone in the room enough context to discuss what happens next. Starting from notes instead? Explore [Muse Slides](https://museai.im/en-US/features/ai-slides).\n\nFor a separate example of an evidence-led presentation, see [SentinelOne Investment Committee Review](https://museai.im/en-US/showcase/1789465940583a6f6d331). Its source material and decision are different from the hypothetical onboarding report above.\n\n**Ready to make your own deck?** [Start with the PDF in MuseWork](https://museai.im/en-US/usecase/pdf-to-presentation); if you're new, follow the sign-up steps when prompted. Prefer working with files on your computer? [Get the MuseWork desktop app](https://museai.im/en-US/download).","/images/pdf-report-to-presentation-flat.webp",3600,2176,1790640000,{"id":67,"slug":68,"title":69,"seo_title":70,"seo_description":71,"summary":71,"cover_alt":72,"author":13,"category":58,"tags":73,"content":74,"cover_url":75,"cover_width":63,"cover_height":64,"is_pinned":22,"is_featured":23,"published_at":65,"modified_at":65},"1790640000000-research-notes-to-executive-presentation","research-notes-to-executive-presentation","How to Turn Research Notes into an Executive Presentation","How to Turn Research Notes into an Executive Presentation | MuseWork","Have interviews, competitor links, and scattered notes? Turn them into an editable leadership deck that makes the recommendation and next step clear.","Minimal editorial graphic with three research-note marks beside the words EXECUTIVE DECK.",[60,18],"Friday's product planning meeting is coming up, and your manager wants a recommendation: should the team test a new customer segment? There is no finished report to upload. Interview notes are in a shared document, a pricing comparison is in a spreadsheet, and teammates have been discussing objections in Slack. You need to make a leadership deck from work that is still in progress.\n\nSuppose two interviewees asked for a way to fit the product into their existing approval workflow, while competitor pricing and a market article hint at an opening. Your manager doesn't need a slide listing every link. They need to know what the team observed, what remains an assumption, whether a small pilot is worth approving, and what would change that recommendation.\n\n## Write the question at the top of your notes\n\nFor example: “Should we run a small pilot with this customer group?” Sort the material into what you've observed, what you think it means, and what you still need to check. Keep the original link or file beside important claims. When sources disagree, show that too.\n\nNow build the deck around the meeting: your recommendation first; the customer problem; the strongest evidence; a small pilot; the biggest risk; and the decision you're asking for. Longer interview excerpts can go at the end. People can ask about the details after they understand the proposal.\n\n## Make the slides easier to decide from\n\n“Customer interviews” is a label. “Test the approval workflow with a small group before expanding” is a point of view. Put that point in the title, and use a short quote or a number beneath it. If only two people mentioned a problem, say “two interviewees,” not “customers want this.” If you cite a competitor's price, note when you checked it.\n\nFinish with a practical ask: Who would run the pilot? What would you test? What result would make you continue—or stop? That is more helpful than ending on a generic summary slide.\n\n## Turn scattered evidence into a decision slide\n\nSuppose your notes say two interviewees described approval steps their team could not skip; a competitor's price was checked on a particular date; an industry article suggests a shift, but has not been verified against your market. A useful slide title is “Run a small pilot before committing to a new segment.” Under it, label the interviews as observations, the pricing and market implication as points to verify, and the pilot as a recommendation. The last slide asks for approval to test with a limited group, not a full launch.\n\n## Draft it in MuseWork\n\nOpen [Research to Presentation](https://museai.im/en-US/usecase/research-to-presentation) and add your notes, files, or links. With your permission, you can connect services such as Notion, GitHub, Slack, Google Drive, and OneDrive, then ask MuseWork to use relevant project notes, discussions, files, or code context you have authorized for this task. Instead of explaining the same project history from scratch, you can start from those materials and focus on what matters now: who the presentation is for, what the meeting needs to decide, and which claims still need checking. With MuseWork's official **Create Slides** skill, pick a template and choose among three outputs: **PowerPoint** for an editable deck, **Online presentation** to view and share on the web, or **Image presentation** for a more visual deck generated page by page. Choose PowerPoint if you need to revise the slide content later. Then select a slide-count **range**—currently 4–8 through 20–24 slides, not an exact number.\n\nTry this:\n\n> Make a leadership PowerPoint from these interview notes, competitor links, and market notes. We need to decide whether to try a small pilot. Start with a tentative recommendation. Show what we actually observed, what we are inferring, and the biggest reason not to proceed. End with a pilot plan and what we would measure. Use the 8–12-slide range and point out claims I should check in the original files.\n\nMuse Slides can help organize a first version, but it doesn't make the underlying research true. Check the quotes, figures, dates, and meanings yourself. If information is missing, say what you would need to learn instead of filling the gap with a confident-sounding claim.\n\nA useful leadership deck makes it possible to say yes, no, or “find out this first.” Starting with one finished report? Use [PDF to Presentation](https://museai.im/en-US/usecase/pdf-to-presentation).\n\nSee how MuseWork brings interviews, support tickets, survey results, usage data, and competitor research into a [product business case for a review meeting](https://museai.im/en-US/showcase/17894669049326ce48089). If your question is where to compete rather than what to improve, explore the [competitive analysis presentation](https://museai.im/en-US/showcase/17894667406798b7280eb).\n\n**Want to try this with your own notes?** [Start a research presentation in MuseWork](https://museai.im/en-US/usecase/research-to-presentation); if you're new, follow the sign-up steps when prompted. Prefer handling your files on a computer? [Get the MuseWork desktop app](https://museai.im/en-US/download).","/images/research-notes-to-executive-deck-flat.webp",{"id":77,"slug":78,"title":79,"seo_title":80,"seo_description":81,"summary":81,"seo_keywords":82,"cover_alt":87,"author":13,"category":88,"tags":89,"content":91,"faq":92,"cover_url":102,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":103,"modified_at":103},"1790553600000-meta-muse-iphone-android-app","meta-muse-iphone-android-app","Meta Muse App for iPhone and Android: What Downloading It Actually Means","Meta Muse App for iPhone and Android: Availability and Download Questions | MuseWork","Where the Meta Muse app is offered on iPhone and Android, why a store listing may not mean access, and a separate mobile AI work option from MuseWork.",[83,84,85,86],"Meta Muse app","muse ai app download","meta muse iPhone","meta muse Android","Meta Muse App for iPhone and Android: What Downloading It Actually Means — MuseWork editorial illustration","AI Tools",[90,18],"Meta Muse","Meta Muse has iPhone and Android apps, introduced at launch. Finding the listing is only the beginning: whether you can start a task depends on the account you sign in with.\n\n## What the phone app can do\n\nMuse from Meta lets eligible users hand off a task, check progress and respond when an action needs approval. If sign-in shows a country warning, installing the app from another source will not grant account access.\n\n## When the app opens but the task does not\n\nIf Muse opens but sign-in shows a country warning, reinstalling it will not change the account's eligibility. If chat works but a connected service is missing, look at that service's permissions instead. There is no need to upload private project files to diagnose a sign-in problem.\n\nFor a longer task, the app can bring you back when a decision is needed. Check the recipient, site or amount before approving an email or purchase; earlier permissions may change when Muse asks again. The useful first test is a small task whose result you can review, not a broad connection to every work account.\n\n## What can an eligible user actually do after signing in?\n\nMuse is not merely an app for asking a question. You can hand off a request in conversation and return when the task needs direction. Email and travel are among the examples, though the available connections depend on the account. The agent uses a dedicated cloud computer and browser rather than your phone physically running each browser step. That distinction matters when a request involves passwords, external websites, or a purchase: a phone may be the place you approve an action even though the work happens elsewhere.\n\nFor a longer-running task, the **Activity log** shows what happened and what you allowed. Approval can be set at different levels, including site- or connector-specific choices. Before accepting a request, open its details and check the recipient, amount, site, or other consequence. An approval you previously granted may affect what happens next, so do not assume every subsequent action will show the same prompt. If the task creates a document, Meta says generated files go to the Library. Before sharing one outside the account, check the recipient and the approval settings.\n\n## What if you want an AI agent on your phone now?\n\nThe more practical question may be what you need to finish. A project manager leaving a meeting could record the decisions and open questions on a phone, then return to the source files and shape a reviewable brief at a desk. That work is more than installing an app: the context needs to survive the move from quick instructions to a finished result.\n\n**MuseWork is a separate AI work product from a different company.** Its iPhone and Android apps provide a mobile way to keep a work task moving. When the work needs files or browser actions on your computer, MuseWork also offers desktop clients for Mac, Windows and Linux. Start with the result you need—such as a short decision brief from meeting notes and research links—then review what comes back before sharing it.\n\n## A better first task than “try everything”\n\nSuppose you need an agenda from notes you can safely share internally. Give an audience, the meeting date and the two decisions that must be made. Ask for a draft with unresolved questions separated from confirmed facts. That lets you check whether the agent carried the context into an actual artifact without granting access to your full mailbox or allowing it to send anything. If you have work files already on your computer, starting that same brief in **MuseWork** offers a separate work-focused path: review the draft in the workspace and choose a mobile or desktop client according to where the materials live. \n\n**[Get MuseWork for iPhone or Android](/en-US/download)**\n\n[Prefer the web? Start the meeting brief in MuseWork](/en-US/chat?scene=meta-muse-iphone-android-app)\n\n## Sources\n\n- [Meta — Introducing Muse (launch and platforms)](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)\n- [Meta Help — Guidance and approval settings](https://www.meta.com/help/artificial-intelligence/1385290430137537/)\n- [Meta Help — Documents and other artifacts](https://www.meta.com/help/artificial-intelligence/2074655449783957/)",[93,96,99],{"question":94,"answer":95},"What can I start on my phone?","A short work brief in MuseWork: give the audience, deadline and source material, then review the draft before sharing.",{"question":97,"answer":98},"Can I download MuseWork on both iPhone and Android?","MuseWork provides both mobile app routes through its download page. The page shows the relevant platform choice for your device.",{"question":100,"answer":101},"Does installing the Meta Muse app guarantee access?","No. Availability may depend on the country, store and account; a listing alone is not confirmation.","/images/meta-muse-iphone-android-app.webp",1790553600,{"id":105,"slug":106,"title":107,"seo_title":108,"seo_description":109,"summary":109,"seo_keywords":110,"cover_alt":114,"author":13,"category":88,"tags":115,"content":116,"faq":117,"cover_url":127,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":103,"modified_at":103},"1790553600000-meta-muse-windows-pc","meta-muse-windows-pc","Meta Muse on Windows: Is There a Desktop App?","Meta Muse on Windows: Is There an App or Only Web Access? | MuseWork","Check the difference between a Meta Muse Windows app and browser access. See a separate Windows desktop work option from MuseWork.",[111,112,113],"Meta Muse Windows","muse ai download windows","meta muse for PC","Meta Muse on Windows: Is There a Desktop App? — MuseWork editorial illustration",[90,18],"Meta Muse works in a Windows browser for eligible accounts. Meta's consumer download page lists Mac and mobile apps, but no native Windows installer. That distinction matters when the files you need are on the PC rather than on the web.\n\n## When the work starts with files on your PC\n\nMuse's web experience lets you assign and review tasks in a browser. Its agent works in Meta's cloud environment, so opening Muse in Chrome or Edge does not give it access to a folder on your PC. The Mac app follows a different permission path for local files and apps.\n\n## A name on the installer is not enough\n\nAn app that wraps the Muse website opens a web session, not local-file access. Muse Code is Meta's developer coding product, with a different job from the personal agent. If you find a Windows installer using the Muse name, check who publishes it before opening it. For a task built around files in Downloads, look for an actual way to share or access those files.\n\n## What you can watch in the browser\n\nIn an eligible Muse account, you can open the agent's browser view during a task, take control when it reaches a page you want to handle yourself, or stop the task. If you're comparing public vendor prices, you can inspect the pages it used and ask for sources beside each figure.\n\nThat browser runs in Meta's cloud environment. It does not read a local Windows folder just because Muse is open in Edge or Chrome. When your inputs are a private spreadsheet and an old deck, choose a tool with a file workflow that fits those materials.\n\n## A Windows job that needs more than a browser tab\n\nImagine a product review due tomorrow. There are research links, two spreadsheets, comments from colleagues and a draft deck. The job is to reconcile the figures, explain what changed, build a concise presentation and leave room for review. **MuseWork** is a separate work product with a Windows desktop client for that kind of job.\n\nMuseWork offers a Windows desktop client. With permission, it can work with local files and browser context and help turn a request and its materials into something you can inspect and revise. You can start from the same work on the web, or keep track of it on your phone; the desktop choice matters most when the files and working context are on that PC.\n\n\n## A Windows task with a finished output\n\nFor a quarterly review, collect the approved revenue spreadsheet, last quarter's slides and five customer notes. In MuseWork's Windows client, the useful request is not “summarize the folder”; it is “explain which figures changed, flag every mismatch with a source, then draft a six-slide review for the sales lead; leave uncertain claims marked for approval.” That turns desktop access into an auditable deliverable. \n\n**[Get MuseWork Desktop for Windows](/desktop)**\n\n[Prefer to begin on the web? Start a brief in MuseWork](/en-US/chat?scene=meta-muse-windows-pc)\n\n## Sources\n\n- [Meta — Introducing Muse (launch and platforms)](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)\n- [Meta — Muse consumer download page](https://ai.meta.com/muse/download/)\n- [Meta Help — Agent browser and takeover](https://www.meta.com/help/artificial-intelligence/2124746764949121/)\n- [Meta for Developers — Muse Code](https://developer.meta.com/ai/products/muse-code/)",[118,121,124],{"question":119,"answer":120},"Is there an official Meta Muse Windows app?","Meta's consumer download page currently lists Mac and mobile apps, not a native Windows installer.",{"question":122,"answer":123},"Can Muse run on a Windows computer at all?","An eligible account can access the browser experience. That differs from installing a native desktop client.",{"question":125,"answer":126},"What is MuseWork for Windows?","A separate MuseWork desktop app for work involving your PC's files and browser, not Meta software.","/images/meta-muse-windows-pc.webp",{"id":129,"slug":130,"title":131,"seo_title":132,"seo_description":133,"summary":133,"seo_keywords":134,"cover_alt":138,"author":13,"category":88,"tags":139,"content":140,"faq":141,"cover_url":151,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":103,"modified_at":103},"1790553600000-meta-muse-region-availability","meta-muse-region-availability","“Meta Muse Is Not Available in Your Country”: What Does That Mean?","Meta Muse Not Available in Your Country? What the Message Means | MuseWork","Understand Meta Muse region and account availability, why a listing may not mean access, and a separate MuseWork option on mobile and desktop.",[135,136,137],"Meta Muse not available in your country","Meta Muse region availability","Muse country or region","“Meta Muse Is Not Available in Your Country”: What Does That Mean? — MuseWork editorial illustration",[90,18],"A Muse product page can load while sign-in says the service is unavailable in your country. Meta began with a limited rollout; availability depends on the account and can change. Your account message is the useful answer when you need to start a task.\n\n## When sign-in stops you\n\nAn app listing tells you where an app can be found, not whether your account can start a task. If you get a country warning after sign-in, another installer or a VPN will not establish eligibility. If chat works but a calendar or file connection does not, check that service's permissions separately.\n\n## If you can open Muse but not finish a task\n\nA country warning at sign-in is different from a calendar connection failing midway through a task. The first calls for an account-availability check; the second may depend on that calendar service's permissions and terms. A screenshot of a friend's working account cannot tell you which services are available to yours.\n\nMuse Spark and Meta AI can also show up in search results. They are not the personal Muse agent described here. If you're unsure which product you have, check the name on the page before entering work documents.\n\n## Why a connected app may still hold up the task\n\nMuse's Connectors let it work with other services, but each service has its own permissions. Custom Connectors are not individually reviewed by Meta, so the provider's privacy terms deserve a look before you connect one. If your account can start a conversation but a task stalls when it needs a calendar or file service, that connection is the place to investigate.\n\nThe initial announcement did not provide a worldwide rollout schedule. If a region warning appears, rely on what the current account screen tells you rather than a dated list or someone else's setup guide.\n\n## When the decision memo is due this week\n\nThe research and meeting notes may already be in front of you. What is missing is a memo that ties the decision to the sources and makes the unresolved numbers visible. **MuseWork is a separate product from Meta Muse.** You can start with those materials and the reader's question, then review a draft before it goes to anyone else.\n\n## Start with the materials you already have\n\nFor a decision memo, start in MuseWork with the audience, three source documents and the decision the reader must make. Request a one-page summary followed by a table of disputed numbers; check sources before sharing. On a phone, the MuseWork download page offers mobile options, while a desktop client is relevant when your source files are on your Mac or Windows machine. \n\n**[Start a decision memo in MuseWork](/en-US/chat?scene=meta-muse-region-availability)**\n\n[Need an app instead? See MuseWork downloads](/en-US/download)\n\n## Sources\n\n- [Meta — Introducing Muse (launch and platforms)](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)\n- [Meta Help — Connectors and permissions](https://www.meta.com/help/artificial-intelligence/1687253048996149/)",[142,145,148],{"question":143,"answer":144},"Does opening the Muse site mean my country is supported?","No. Reading a public page and receiving account-level service access are separate things.",{"question":146,"answer":147},"When will the region warning go away?","There is no universal date for every account. Check the latest availability notice and your account message.",{"question":149,"answer":150},"Will a VPN solve the region message?","It is not evidence of account eligibility and may conflict with service terms. Check the current service eligibility instead.","/images/meta-muse-region-availability.webp",{"id":153,"slug":154,"title":155,"seo_title":156,"seo_description":157,"summary":157,"seo_keywords":158,"cover_alt":162,"author":13,"category":88,"tags":163,"content":164,"faq":165,"cover_url":175,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":103,"modified_at":103},"1790553600000-meta-muse-mac-files-permissions","meta-muse-mac-files-permissions","Meta Muse on Mac: What Happens When an Agent Can Use Your Files and Apps?","Meta Muse on Mac: Files, Apps and Permissions After Connect 2026 | MuseWork","What Meta Muse can access on a Mac, which permissions are controlled in macOS, and how a separate MuseWork desktop workflow handles project files.",[159,160,161],"Meta Muse Mac permissions","Meta Muse Mac files","Muse computer use Mac","Meta Muse on Mac: What Happens When an Agent Can Use Your Files and Apps? — MuseWork editorial illustration",[90,18],"A Mac agent is different from a browser tab. Meta's Mac version of Muse can work with files and supported native apps after a user grants access; its September 2026 documentation describes permission controls in macOS for Full Disk Access, Automation and notifications. The relevant question is not simply whether Muse is “on Mac,” but what information a particular task needs and what the agent is allowed to change.\n\n## Access is a choice, not a single switch\n\nOn a Mac, a request may involve files, forms, or information in Messages, Calendar and Notes. The precise path depends on the permission granted, the app and the task. Mac system settings can revoke access to files or automation. Those interactions can include information from apps and services, even screenshots. Whether those interactions can be used to improve Meta's models is a separate setting. Permission to read a file is not a blanket reason to let an agent send mail or modify unrelated folders.\n\nMeta also previewed Muse on glasses and a dedicated device at Connect; those announcements do not expand the Mac app's present permissions.\n\n## What each permission changes in practice\n\n**Full Disk Access** is an operating-system permission covering all files on the Mac, even though the agent is meant to use the files and app information you assign to the task. Do not mistake that stated task limit for a macOS folder-level grant. **Automation** can allow actions in local apps, including examples such as sending messages or deleting notes. **Notifications** can report that a background task has finished. These can be revoked separately in System Settings → Privacy & Security. Decide whether a task needs any of them before accepting an OS prompt.\n\nNext consider the flow of information. Task interactions can include information from connected apps and screenshots of your screen. Third-party apps have their own privacy policies, so check what information a task uses across them. The setting that controls whether Muse interactions are used to improve Meta AI models is separate from the Mac's file permission; Meta says it is enabled initially and can be turned off. Its future **Confidential VM** announcement is not a feature available in the Mac app today. These distinctions are useful even if you never choose to grant full-disk access.\n\n## From a local folder to a reviewable result\n\nSay you have a folder of research PDFs, a spreadsheet with last month's results and notes for a team review. The useful outcome isn't “AI accessed my files”; it is a brief whose claims can be checked against those sources, followed by a presentation you can revise with your team. **MuseWork**, a separate product, offers a Mac desktop client for work starting from local files and browser context with your permission. Give the project goal, the relevant files and the audience; inspect the proposed conclusions before turning them into slides.\n\nWhen you leave your desk, MuseWork's mobile app can help you follow the work. The [download options in MuseWork](/en-US/download) show Mac, iPhone and Android separately. Choose the route that fits where the project materials are.\n\n## A bounded experiment for a real work folder\n\nPlace a public sample report, a small spreadsheet and a draft outline in a test folder. Ask for a brief identifying three conflicting figures, with each claim tied to a filename and a proposed slide order. Observe any permission prompt, inspect which apps are invoked, review the result, then revoke permissions you no longer need. This tests the workflow without sending private customer files as a trial. If the goal is a reusable presentation from project documents, MuseWork's Mac desktop app can be assessed on the same criteria: materials in, sources checked, editable deliverable out. The final check is what the app actually accessed and what the team can use.\n\n**[Get MuseWork Desktop for Mac](/desktop)**\n\n[Prefer to begin on the web? Start a research brief in MuseWork](/en-US/chat?scene=meta-muse-mac-files-permissions)\n\n## Sources\n\n- [Meta Help — Files and apps on Mac](https://www.meta.com/help/artificial-intelligence/1126304576638594/)\n- [Meta Help — Muse data controls](https://www.meta.com/help/artificial-intelligence/2225571704857152/)",[166,169,172],{"question":167,"answer":168},"Does Meta Muse automatically see every file on a Mac?","Access depends on the permissions granted in macOS and the task; check the current app settings.",{"question":170,"answer":171},"Can Mac permissions be removed?","Yes. Full Disk Access, Automation and notifications can be changed in macOS Privacy & Security.",{"question":173,"answer":174},"Is MuseWork the Mac app from Meta?","No. MuseWork is an independent product with its own Mac download.","/images/meta-muse-mac-files-permissions.webp",{"id":177,"slug":178,"title":179,"seo_title":180,"seo_description":181,"summary":181,"seo_keywords":182,"cover_alt":186,"author":13,"category":88,"tags":187,"content":188,"faq":189,"cover_url":199,"cover_width":20,"cover_height":21,"is_pinned":22,"is_featured":23,"published_at":103,"modified_at":103},"1790553600000-meta-muse-app-vs-web","meta-muse-app-vs-web","Meta Muse App vs Web: Start on Your Phone or at Your Desk?","Meta Muse App vs Web: Which Fits the Task? | MuseWork","Compare Meta Muse mobile app and web for quick handoffs and longer tasks, then see how MuseWork carries work between phone, browser and desktop.",[183,184,185],"Meta Muse app vs web","Muse AI web","Meta Muse mobile vs browser","Meta Muse App vs Web: Start on Your Phone or at Your Desk? — MuseWork editorial illustration",[90,18],"An app can be installed and a website can be opened, but the better starting point depends on what you need to do. Meta introduced Muse across mobile and web at launch, and later added a Mac app. The phone is convenient for issuing a quick instruction or checking on a task; a browser gives you more room to read and review the result. Neither route overrides a region or account restriction.\n\n## A quick request versus a working session\n\nIf a message arrives while you are travelling, the phone is the natural place to capture the request and respond to an approval prompt. If you're reconciling several sources into a report, a larger screen makes it easier to read the underlying material and inspect the answer. Some delegated work can continue after the app closes; that does not make every connector or action available on every account and device. The Mac app introduces a separate question: access to local files and native apps, which is not identical to a web session. Mac permissions deserve a separate check before you authorize access.\n\nApp-store visibility and account eligibility do not settle which screen is best for doing the work.\n\n## What Meta's screens share—and what remains distinct\n\nAfter you hand Muse a longer task, it can keep working on its cloud computer while the app is closed. If the agent opens a page, you can bring up its browser view, take control, or stop the task. For an eligible account, the question is therefore more precise than “app or web?”: where will you start the request, where will you inspect the agent's steps, and where will you review the result? A Mac-native task adds a separate layer of OS permissions for local files and apps. Meta has not said that every surface exposes identical controls for every user.\n\nA finished document or image lands in the Library; check the permissions and intended recipient before sharing it outside your account. The **Activity log** helps reconstruct actions and past permissions; **Upcoming** lists scheduled tasks. Those are better checkpoints than assuming a notification means the content is accurate. For example, an agent may have successfully opened pages but relied on an outdated figure. In a browser at your desk, compare the final statement with the underlying source rather than stopping at “task complete.”\n\n## When the work continues at your desk\n\nTake an ordinary team task: someone sends you notes from a customer call on your phone. You want the open issues grouped, supporting research added, and a short proposal ready for a meeting. The first instruction can happen on mobile; the evidence review and final document may happen in a browser or on a computer with the relevant files. The important thing is keeping the task, sources and feedback connected as it moves.\n\n**MuseWork is a separate AI work partner from a different company.** It offers a web workspace, iPhone and Android apps, and desktop clients for Mac, Windows and Linux. A phone can be a useful place to start or follow the job; for local files and browser actions, the desktop client brings the work into your computer environment with permission. Your team can review the proposed result instead of treating the first answer as final.\n\n## A handoff you could copy into a real task\n\n“Prepare a one-page brief for Thursday's product review. Use the attached meeting notes and the approved Q3 sheet; mark figures not supported by either. Draft three recommendations, but do not send the result to anyone. Ask me before using an outside service.” The phone is a good place to capture the request and approve a well-described step; a computer is better for checking the sheet against the brief. In MuseWork, a separate work partner, that same structure—goal, materials, audience, output and review boundary—makes the mobile-to-desktop route meaningful. The next step depends on where the source materials live and how the result will be reviewed.\n\n**[Start the one-page brief in MuseWork](/en-US/chat?scene=meta-muse-app-vs-web)**\n\n[See MuseWork mobile and desktop downloads](/en-US/download)\n\n## Sources\n\n- [Meta — Introducing Muse (launch and platforms)](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)\n- [Meta Help — Files and apps on Mac](https://www.meta.com/help/artificial-intelligence/1126304576638594/)\n- [Meta Help — Agent browser and takeover](https://www.meta.com/help/artificial-intelligence/2124746764949121/)\n- [Meta Help — Guidance and approval settings](https://www.meta.com/help/artificial-intelligence/1385290430137537/)\n- [Meta Help — Documents and other artifacts](https://www.meta.com/help/artificial-intelligence/2074655449783957/)",[190,193,196],{"question":191,"answer":192},"Is the Muse web experience the same as its Mac app?","No. The Mac app has permission-based local file and app capabilities that need separate consideration.",{"question":194,"answer":195},"Can I begin a MuseWork task on my phone?","MuseWork offers iPhone and Android apps as well as web and desktop routes. Choose the route that matches the materials you need.",{"question":197,"answer":198},"Does switching from app to web bypass a region restriction?","No. An account's eligibility is a separate question from the screen used to open it.","/images/meta-muse-app-vs-web.webp",{"id":201,"slug":202,"title":203,"seo_title":204,"seo_description":205,"summary":205,"cover_url":206,"cover_alt":207,"cover_width":20,"cover_height":21,"author":13,"category":33,"tags":208,"is_pinned":22,"is_featured":23,"published_at":209,"modified_at":209,"content":210},"1790208000000-gpt-6-sol-luna-vs-claude-opus-5-5-cost-per-task","gpt-6-sol-luna-vs-claude-opus-5-5-cost-per-task","GPT-6 Sol, Luna, or Claude Opus 5.5? Compare the Work You Can Actually Use","GPT-6 Sol vs Claude Opus 5.5: Cost per Finished Task | MuseWork","GPT-6 Sol and Luna launched alongside Claude Opus 5.5. Compare API prices, independent tests, and the cost of a usable work product—not tokens alone.","/images/gpt-6-sol-luna-vs-claude-opus-5-5-cost-per-task.webp","OpenAI, Claude and MuseWork marks in three equally sized square tiles for a model comparison article.",[],1790208000,"OpenAI and Anthropic released three models on September 22, 2026. The price tags are easy to quote; the cost of a report your team can actually send is harder to see. If an inexpensive draft needs two reruns and an hour of fact-checking, the cheapest token is not necessarily the cheapest finished task.\n\n## What changed on September 22\n\nOpenAI introduced GPT-6 Sol for demanding coding and agentic work and GPT-6 Luna for focused, high-volume tasks. Its launch post says API prices are 50% below **GPT-5.6 promotional prices**. At publication, OpenAI lists Sol at $2 input and $10 output per million tokens, and Luna at $0.10 and $0.50. Anthropic introduced Claude Opus 5.5 on the same day. It lists $4 input and $20 output per million tokens, a 20% reduction from Opus 5; its $0.20 cache-read price is 60% lower than before. Anthropic's estimate of 40% lower cost on typical workloads is a vendor estimate, not another reduction in token list price. Caching, reasoning settings and tools can change the bill.\n\n## Why the token price is only the first line of the budget\n\nConsider an illustrative job that uses 100,000 input tokens and produces 10,000 output tokens, with no cache or tools. At the standard listed rates checked September 24, the model charge is about **$0.30 for GPT-6 Sol**, **$0.015 for GPT-6 Luna**, and **$0.60 for Claude Opus 5.5**. This is arithmetic on a hypothetical workload, not a measured result or a fair quality comparison. If a Luna draft needs repeated runs, or an Opus draft saves an hour of review, the answer changes. Even a sound first-pass estimate can be distorted by the mix of input and output: output tokens cost more than input tokens for all three models.\n\nOpenAI's Sol and Luna documentation also says requests above 272,000 input tokens are billed at higher rates for the **entire request**, despite the much larger advertised context window. Fast processing and regional processing have separate price adjustments. Anthropic highlights cheaper cache reads for Opus 5.5; the benefit depends on whether a team repeatedly reuses the same context. Those are procurement details for an actual workflow, not reasons to choose a model solely from a headline price.\n\n## What the benchmarks actually measure\n\nArtificial Analysis reported Opus 5.5 at 58 on its Intelligence Index at max effort, its highest measured score at the time. That is useful independent evidence about a specified evaluation. It is not a head-to-head verdict on your team's supplier memo, sales analysis or customer brief. Sol and Luna have a different price/performance proposition, and a result obtained at one reasoning setting should not be placed next to another setting as though they were the same test. A long context window also does not certify that every figure in a long document was found and cited correctly.\n\nSuppose your team prepares a weekly competitor update from three product pages, two PDFs and a pricing spreadsheet. The deliverable is a one-page recommendation with links, a change log and numbers that reconcile. A model can produce fluent prose while missing the changed price in a footnote. That miss matters more than how impressive the first paragraph sounds.\n\n## A realistic split across three tasks\n\nFor a weekly report that synthesizes conflicting evidence, track whether the model preserves source links and flags unresolved numbers. For a high-volume extraction job, count field-level errors and reruns; low per-call cost can be compelling here. For a presentation outline, check whether the recommendations follow from the supplied data and whether an editor can use the structure without rewriting it. The same model need not win all three. A decision memo can recommend one default, one exception path and a date to retest when the workload or prices change.\n\nThe most informative table is therefore not just “price / score.” Use columns for input packet, accepted-result criteria, first-pass success, rerun count, review minutes, provider charges and remaining risks. That table makes a cheaper model's strengths visible without hiding its correction costs—and gives a higher-priced model a chance to justify its place through a better finished result.\n\n## Measure cost per accepted deliverable\n\nRun each candidate on the same three to five anonymized tasks, with the same input files and an agreed output format. Record whether the draft passes the actual acceptance check: correct figures, traceable sources, required sections, no unsupported assertions and edits a colleague can make. Then include API calls, tool charges, failed attempts, revisions and human review time in the cost of each accepted result. If one model needs a second pass on half the tasks, calculate that cost rather than treating every first answer as a success.\n\nA useful decision memo separates three outcomes: use a stronger model for exception-heavy work; use a lower-cost model for repeatable extraction; or keep the current setup until the gap is meaningful. It should include examples of failures, not just an average score.\n\n## Turn the comparison into a decision brief\n\nGive MuseWork the public announcements, your approved sample materials and the criteria your team uses to sign off a deliverable. Ask it to organize the claims, identify mismatched testing conditions and draft a small trial plan and reviewable decision brief. A ready-to-use request: “Using these model announcements, invoices and three anonymized briefs, create a comparison matrix for source accuracy, accepted-output rate, revision time and total cost. Flag what still needs a real test.”\n\nFor background on the earlier flagship release, read [the GPT-6 Astra article](https://museai.im/en-US/blog/gpt-6-astra-ai-agent-capabilities).\n\n### From these numbers to your next deliverable\n\nBring your own notes, reports or source links to MuseWork and ask for a brief your team can review. [Start a research task](https://museai.im/en-US/chat?scene=deep-research), or [see how Deep Research works](https://museai.im/en-US/features/deep-research).\n\nKeep the same work at hand when you leave your desk. [Get MuseWork for iPhone or Android](https://museai.im/en-US/download), or [install MuseWork Desktop](https://museai.im/desktop) to work with local files with your permission.\n\n## Sources\n- OpenAI model documentation: [GPT-6 Sol](https://developers.openai.com/api/docs/models/gpt-6-sol) and [GPT-6 Luna](https://developers.openai.com/api/docs/models/gpt-6-luna), checked September 24, 2026.\n- OpenAI launch announcement, September 22, 2026: [Introducing GPT-6 Sol and Luna](https://openai.com/index/introducing-gpt-6-sol-and-luna/)\n- OpenAI API price list, checked September 24, 2026: [API pricing](https://developers.openai.com/api/docs/pricing)\n- Anthropic launch announcement, September 22, 2026: [Introducing Claude Opus 5.5](https://www.anthropic.com/claude-opus-5-5)\n- Artificial Analysis independent assessment, September 22, 2026: [Independent assessment of Claude Opus 5.5](https://artificialanalysis.ai/articles/claude-opus-5-5)",{"id":212,"slug":213,"title":214,"seo_title":215,"seo_description":216,"summary":216,"cover_url":217,"cover_alt":218,"cover_width":20,"cover_height":21,"author":13,"category":88,"tags":219,"is_pinned":22,"is_featured":23,"published_at":209,"modified_at":209,"content":220},"1790208000000-amazon-seller-assistant-claude-plugin-seller-operations","amazon-seller-assistant-claude-plugin-seller-operations","Amazon Seller Assistant's Claude Plugin Is Here. What Should Sellers Do With Their Data?","Amazon Seller Assistant Claude Plugin: What US Sellers Can Do | MuseWork","Amazon added persistent workflows and a Selling Partner plugin for Quick and Claude beta. Learn the US availability, approvals, and how to review your own inventory and ad data.","/images/amazon-seller-assistant-claude-plugin-seller-operations.webp","Amazon, Claude and MuseWork marks in three equally sized square tiles on a light background.",[],"A seller sees two familiar problems on Monday morning: one product may stock out, while ad spend on another keeps rising without a clear margin story. Amazon's September 23, 2026 Seller Assistant update is relevant because it connects ongoing signals to proposed actions. It also raises a separate question: what can you learn from the reports and supplier files your team already has?\n\n## What Amazon announced\n\nAmazon says Seller Assistant now retains business context over time and can support continuous workflows around inventory, pricing and other routine tasks, with seller-defined guardrails, approvals and audit trails. A new Selling Partner plugin brings Amazon seller information and capabilities into Amazon Quick and, **in beta**, Anthropic's Claude. These are Amazon's features; an action taken through them remains subject to the seller's authorization and Amazon's controls.\n\nAvailability matters. Amazon says the plugin beta is for sellers in its **U.S. stores**; expansion to other markets is a plan, not a current launch. Its separate offer of a 12-month Amazon Quick Plus subscription for primary account holders worldwide is not evidence that the Claude plugin is available worldwide. Check Amazon's current terms before relying on either offer.\n\nFor sellers comparing access, two details are easy to miss. Amazon says the U.S.-store plugin beta can connect to Claude in roughly a minute without coding; that is Amazon's setup path, not a general integration instruction. Its separate 12-month Quick Plus offer is available for eligible primary account holders globally to sign up through December 31, 2026, and can include two additional coworkers. The offer's geographic reach does not change the plugin beta's U.S.-store scope.\n\n## From a prompt to a standing workflow\n\nAmazon describes a shift from asking Seller Assistant a question to setting a routine watch. A seller could previously request a restock plan or a price change; the new workflows are meant to keep checking thresholds, competitive prices and account-health alerts. The seller defines guardrails, can review proposed actions, and has an audit trail. That distinction matters: a watch can surface a problem before someone opens a dashboard, but choosing to act on a restock or pricing recommendation still belongs to the merchant.\n\nAmazon's Canvas adds a second way to work with the same account context. A seller can explore pricing scenarios or diagnose a campaign in a visual workspace built from live account data. The Selling Partner plugin then brings Seller Assistant's intelligence into Amazon Quick and, in beta, Claude. Amazon says the plugin can bring listing contributions, inventory, sales analytics and performance metrics into those tools, with boundaries on access and human approval for actions. \n\n## From live account signals to an operations review\n\nSeller Assistant may be the natural place to review a live Amazon account or authorize an account change. A monthly operations review can start elsewhere: take a sales and inventory export you are allowed to share, an ad report, supplier lead times and your margin floor. Which SKUs risk running out before the next delivery? Which campaigns show higher spend without enough contribution margin? Which supplier assumption needs a call before increasing the order?\n\n## An operations review that connects the missing pieces\n\nSuppose a product's stock covers 18 days at the current sales pace and a supplier normally needs 25 days. That is a potential gap, but a good review asks whether a promotion is scheduled, whether other stock is already inbound and how variable the supplier lead time has been. Then look at advertising: if spending rose, compare it with sales, contribution margin and the date of the price change. The result is not “automatically order more.” It is a prioritized list: products to verify today, ad campaigns to examine, and supplier questions that affect a purchase decision.\n\nA useful one-page output could have four parts: **SKU and source period; observed risk; calculation or cited row; next owner and unanswered question**. That structure makes it easy for an operations manager to challenge an assumption before taking action. MuseWork's role in this article is to turn the exports, ad reports and supplier terms you provide into that cross-document brief. Seller Central remains the place to confirm live figures and approve account changes.\n\n## Build a seller operations brief from your own reports\n\nCollect only the exports and documents you are authorized to use. State the reporting dates, SKU identifiers, currencies, planned promotions, lead times and minimum acceptable margin. Ask for a shortlist of inventory risks, an ad-and-margin check, possible causes, missing data and questions for the next supplier conversation. Have the team confirm the numbers against Seller Central before it approves any action.\n\nFor the next meeting, you can ask: “Using my last 30 days of sales and stock exports, ad spend by SKU, supplier lead times and minimum margin, list the products at risk of stocking out. Cite the rows behind each finding, explain missing inputs and draft a one-page brief for the operations team.”\n\n### Prepare the next seller review\n\nBring your own notes, reports or source links to MuseWork and ask for a brief your team can review. [Start a research task](https://museai.im/en-US/chat?scene=deep-research), or [see how Deep Research works](https://museai.im/en-US/features/deep-research).\n\nKeep the same work at hand when you leave your desk. [Get MuseWork for iPhone or Android](https://museai.im/en-US/download), or [install MuseWork Desktop](https://museai.im/desktop) to work with local files with your permission.\n\n## Sources\n\n- Amazon official announcement, September 23, 2026: [Amazon's Seller Assistant and Selling Partner plugin announcement](https://www.aboutamazon.com/news/innovation-at-amazon/seller-assistant-plugin-amazon-quick-claude)\n- GeekWire reporting, September 23, 2026: [GeekWire report on external AI tools for Amazon sellers](https://www.geekwire.com/2026/amazon-opens-its-seller-tools-to-outside-ai-agents-starting-with-anthropics-claude/)",{"id":222,"slug":223,"title":224,"seo_title":225,"seo_description":226,"summary":226,"cover_url":227,"cover_alt":228,"cover_width":20,"cover_height":21,"author":13,"category":33,"tags":229,"is_pinned":22,"is_featured":23,"published_at":209,"modified_at":209,"content":230},"1790208000000-grok-4-7-knowledge-work-evaluation","grok-4-7-knowledge-work-evaluation","Grok 4.7 Looks Strong on Knowledge Work. What Would It Do With Your Brief?","Grok 4.7 for Knowledge Work: What the Benchmarks Mean | MuseWork","Grok 4.7 improves on agentic knowledge-work tests. See what independent results show, where comparisons break down, and how to assess a real business brief.","/images/grok-4-7-knowledge-work-evaluation.webp","SpaceXAI, Grok and MuseWork marks in three equally sized square tiles for a knowledge-work evaluation article.",[],"A benchmark can tell you that a model is getting better at multi-step work. It cannot tell you whether it will catch the conflicting price in your supplier documents before your manager sees the recommendation. That is the practical question behind Grok 4.7, released by SpaceXAI on September 21, 2026.\n\n## What Grok 4.7 adds\n\nSpaceXAI describes Grok 4.7 as a model for coding and knowledge work, designed to stay with difficult tasks longer, check its work more carefully and handle extended context. Those are the developer's claims, not a guarantee about any particular procurement, finance or legal document.\n\n## Which Grok 4.7 numbers matter for office work?\n\nSpaceXAI's release table spans coding, electrical engineering, multi-hour office work, legal work and clinical reasoning. Those are different tests; a higher coding result does not make a supplier memo accurate. Its AA Briefcase v1.1 result is 1,657, versus 1,546 for Grok 4.6 under the settings shown. The same table lists a competing model at 1,678, a useful reminder that this is an improvement story rather than an across-the-board win. SpaceXAI also shows 1,695 Elo for Grok 4.7 on GDPval, a professional-work measure, versus 1,605 for Grok 4.6. The independent Artificial Analysis evaluation reports meaningful gains on long-horizon knowledge work at xhigh effort, while other tasks moved less. Keep track of whose test and which effort setting a figure comes from.\n\n## The brief that exposes the difference\n\nImagine a buyer comparing three suppliers for a seasonal product. One quote gives a lower unit price but a later delivery date; another includes freight only above a minimum order; a third has a return clause hidden in an attachment. The real output is not a summary of three PDFs. It is a decision note showing which option meets the deadline and margin target, where the evidence is, and which unanswered question could change the recommendation.\n\nThat example probes several things a single benchmark number obscures: can the model distinguish a fact from an assumption, keep units consistent, notice a conflicting clause and say when it needs a human decision? A large context limit does not by itself answer any of those questions.\n\n## Test with a packet the team knows well\n\nChoose a small, approved set of documents whose correct answers are already known. Write down the acceptance criteria first: exact price and currency, delivery dates, source links or page references, exclusions, a recommendation tied to the constraints, and a separate list of unresolved points. Run the same packet in your existing workflow and in Grok 4.7 where you have access. Keep the failed cases and the time a reviewer spent fixing them. For sensitive material, use redacted samples and your organization's data-sharing rules.\n\nIf the result is a clearer sourcing brief with fewer missing conditions, expand the pilot to another task. If it merely sounds more confident while leaving out the freight rule, you have found a useful limit without switching your whole workflow.\n\n## What an answer must show before anyone acts\n\nIn a supplier comparison, put the unit price and currency beside order minimums, freight, lead time and return terms. If one quote is dated after another, the brief should say which price is current. If shipping is not included, the recommendation should not pretend the landed cost is known. A manager should be able to trace each statement to a page or spreadsheet row, mark the unresolved terms and decide what to ask next. The point is to turn model news into a reviewable business decision.\n\nSpaceXAI lists Grok 4.7 in Cursor, Grok Build and its API, and GitHub announced Copilot availability. The starting API rate is $2 per million input and $6 per million output tokens; SpaceXAI also describes a faster variant at a higher rate. These channels and prices help plan a trial, while actual access and the final bill depend on the service and configuration used.\n\n## Put the evidence in one place\n\nMuseWork can take the public release, independent assessment and the documents you are authorized to provide, then organize an evaluation plan or a source-backed decision brief. Try this request: “Compare these three supplier quotes against a November delivery deadline and a 25% gross-margin floor. Cite each quote, show assumptions separately, flag inconsistent terms and draft a one-page recommendation for review.”\n\n### Turn the quotes into a decision\n\nBring your own notes, reports or source links to MuseWork and ask for a brief your team can review. [Start a research task](https://museai.im/en-US/chat?scene=deep-research), or [see how Deep Research works](https://museai.im/en-US/features/deep-research).\n\nKeep the same work at hand when you leave your desk. [Get MuseWork for iPhone or Android](https://museai.im/en-US/download), or [install MuseWork Desktop](https://museai.im/desktop) to work with local files with your permission.\n\n## Sources\n\n- SpaceXAI announcement, September 21, 2026: [Introducing Grok 4.7](https://x.ai/news/grok-4-7)\n- Artificial Analysis independent test, September 21, 2026: [Benchmarking Grok 4.7](https://artificialanalysis.ai/articles/benchmarking-grok-4-7)\n- GitHub rollout notice, September 21, 2026: [GitHub announcement on Grok 4.7 in Copilot](https://github.blog/changelog/2026-09-21-grok-4-7-is-now-available-in-github-copilot/)",{"id":232,"title":233,"seo_title":234,"seo_keywords":235,"slug":243,"summary":244,"cover_url":245,"cover_alt":246,"author":13,"category":33,"tags":247,"is_pinned":22,"is_featured":23,"published_at":250,"modified_at":250,"content":251,"faq":252},"1790092800000-gpt-6-astra-ai-agent-capabilities","GPT-6 Astra Released: A Major Upgrade for Complex AI Agent Work","What Is GPT-6 Astra? Release Date, Capabilities, and How to Use It | MuseWork",[236,237,238,239,240,241,242],"GPT-6 Astra","GPT-6 release date","what is GPT-6","how to use GPT-6","GPT-6 capabilities","GPT-6 AI Agent","MuseWork GPT-6","gpt-6-astra-ai-agent-capabilities","GPT-6 Astra launched in September 2026. Learn its release date, core capabilities, how to use it, and what changes as MuseWork's primary Agent model.","/images/gpt-6-astra-ai-agent-capabilities-v2.webp","GPT-6 Astra improving complex-task understanding, tool use, and long-running AI Agent execution",[236,248,249,18],"GPT-6","AI Agent",1790092800,"OpenAI released GPT-6 Astra on September 3, 2026. The upgrade is not about making AI capable of work for the first time. Its focus is improving computer use, tool calling, software engineering, and reliable execution across longer, more complex tasks.\n\n**MuseWork now uses GPT-6 Astra as the primary foundation model for its Agent.** You can assign research, data analysis, presentation, website, or coding work without configuring an API, model, or runtime environment.\n\n## GPT-6 Astra at a glance\n\n- **What is it?** OpenAI's new frontier model for computer use, tool calling, professional knowledge work, and long-running tasks.\n- **When was it released?** September 3, 2026 in the United States.\n- **What improved?** Understanding complex goals, maintaining multi-step workflows, using tools, and producing reviewable results.\n- **How can you use it?** Through OpenAI's announced channels as availability expands, or directly in MuseWork for real work.\n- **What changes in MuseWork?** GPT-6 Astra is the Agent's primary foundation model, working with context management, tool orchestration, permissions, and result verification.\n\n## What is GPT-6 Astra?\n\nGPT-6 Astra is OpenAI's new model for complex, tool-enabled work. Official materials emphasize computer use, browsing, software engineering, professional knowledge work, and multi-step processes rather than conversation alone.\n\nEarlier models could already complete work. The more accurate change is that Astra further improves an AI Agent's ability to hold onto a goal, use tools correctly, respond to feedback, and deliver results over a longer execution.\n\nArtificial Analysis reported clear gains over the previous generation in long-horizon knowledge work and automation evaluations, while noting that some professional-task and presentation-quality measures did not improve at the same rate. Its value should therefore be judged through the quality of completed tasks, not a blanket claim that it is simply “smarter.”\n\n## When was GPT-6 Astra released?\n\nOpenAI lists **September 3, 2026** as the release date. Access began with a limited set of organizations and is expanding through paid ChatGPT plans, the OpenAI API, Microsoft Azure, and AWS.\n\nAvailability can differ by product, region, and account. Developers should check the latest official console information. People who want to complete work directly can use GPT-6 Astra through MuseWork's Agent workflow.\n\n## What are GPT-6 Astra's core capabilities?\n\n### 1. Understanding complex requirements\n\nA capable Agent must identify the goal, source scope, constraints, and definition of done from natural language. Better understanding reduces early-stage drift.\n\n### 2. Maintaining long-running workflows\n\nComplex work crosses research, analysis, production, verification, and revision. Astra is designed to preserve the main objective across those steps and continue when requirements change.\n\n### 3. Computer use and tool calling\n\nAn Agent may need browsers, code environments, file systems, and specialist tools. Stronger tool use helps research, data processing, web production, and code changes operate as one workflow.\n\n### 4. Delivering professional outputs\n\nCompletion usually means an editable, inspectable report, document, spreadsheet, presentation, website, or code change—not only advice or a draft.\n\n### 5. Working across files and sources\n\nWithin authorized boundaries, an Agent must combine multiple files, identify related evidence, resolve conflicts, and organize findings around one objective.\n\n## How do you use GPT-6 Astra?\n\nDevelopers can follow availability in the OpenAI API, paid ChatGPT plans, Microsoft Azure, and AWS. Direct model use normally requires API configuration, runtime tools, and safety controls.\n\nTo use GPT-6 for finished work in MuseWork:\n\n1. **Describe the goal**, audience, and desired outcome.\n2. **Provide the necessary material** through files, webpages, or authorized local tools.\n3. **Set boundaries** for reading, editing, and actions that require approval.\n4. **Define completion**, such as a sourced report, editable deck, tested code, or working page.\n5. **Review and revise** while the Agent continues from the same context.\n\nYou can start with [MuseWork Deep Research](/en-US/features/deep-research), then turn verified findings into an editable deck with [AI Slides](/en-US/features/ai-slides) or the [research-to-presentation workflow](/en-US/usecase/research-to-presentation).\n\n> **[Try GPT-6 Astra in MuseWork](/en-US/chat?scene=gpt-6-astra-complex-task&article_slug=gpt-6-astra-ai-agent-capabilities&locale=en-US&cta_position=mid_how_to_use&cta_copy_id=try_gpt6_astra&cta_destination=chat&task_scene=gpt-6-astra-complex-task)**\n\n## GPT-6 Astra vs GPT-5.6 Sol\n\nBoth generations can understand, generate, call tools, and support Agent execution. Astra's incremental gains center on long-running tasks, computer use, software engineering, and complex tool coordination. It did not make work execution possible for the first time.\n\nPublic evaluations show meaningful gains in some long-horizon knowledge and automation tasks, not universal leadership on every measure. Test the model against your own requirements for quality, speed, cost, and safety.\n\n## How GPT-6 Astra improves MuseWork\n\nBuilding on existing Agent execution, MuseWork now uses GPT-6 Astra as its primary foundation model to strengthen the understanding, execution, and delivery of long-running, multi-tool work.\n\nFor deep research, the Agent can keep searching, filtering, and cross-checking sources around one question. For spreadsheets and presentations, findings, data, and formatting requirements can stay connected. For development, it can inspect a project, implement changes, run tests, and report evidence. [MuseWork Desktop](/en-US/download) can also use authorized local files and tools.\n\n## Why the model alone is not enough\n\nARC Prize reported a score of 62.7% for GPT-6 Astra in its standard environment and 99.9% with a Provider Adapter that preserves reasoning state and compresses long conversations. ARC Prize described both as leading results while emphasizing that reaching the benchmark ceiling does not prove artificial general intelligence.\n\nThe gap illustrates a practical point: the model shapes the capability ceiling, while the surrounding system determines whether that capability becomes a dependable result. MuseWork manages context, task decomposition, tools, permissions, recovery, and verification around Astra.\n\n## Safety, permissions, and verification\n\nOpenAI classifies GPT-6 Astra as its first model to reach the company's “Critical” cybersecurity capability threshold. Its system card reports stronger compliance with safety constraints than GPT-5.6 Sol, but also describes monitoring challenges under some adversarial conditions.\n\nThat does not mean ordinary use is inherently dangerous. It means that more capable computer use and longer autonomy make product-level controls more important: narrow authorization, protection of sensitive data, confirmation for irreversible actions, activity records, and independent verification.\n\n## From capable execution to more reliable completion\n\nGPT-6 Astra pushes frontier-model competition further toward the quality of completed complex tasks. The model must not only understand and generate; it must preserve the objective, use tools correctly, respond to feedback, and leave a result that can be inspected and reused.\n\nThe real measure of the upgrade is whether a task reaches completion with fewer interruptions, clearer control, and a useful deliverable.\n\n## Use GPT-6 Astra in MuseWork now\n\nGPT-6 Astra is now the primary foundation model in MuseWork's Agent workflow. You do not need to request separate API access or configure a tool environment.\n\nStart with one real assignment: request a verified research report, analyze business data and build a presentation, or provide a code project and acceptance criteria for implementation and testing.\n\n> **[Give a real task to MuseWork powered by GPT-6 Astra](/en-US/chat?scene=gpt-6-astra-real-task&article_slug=gpt-6-astra-ai-agent-capabilities&locale=en-US&cta_position=final&cta_copy_id=start_real_task&cta_destination=chat&task_scene=gpt-6-astra-real-task)**\n\n## Frequently asked questions\n\n### What is GPT-6 Astra?\n\nGPT-6 Astra is OpenAI's September 2026 frontier model focused on computer use, tool calling, software engineering, and long-running complex tasks.\n\n### When was GPT-6 Astra released?\n\nOpenAI released GPT-6 Astra on September 3, 2026. Availability can vary by product, region, and account.\n\n### How can I use GPT-6 Astra?\n\nDevelopers can follow OpenAI API, paid ChatGPT, and cloud-platform availability. Users can also access it as MuseWork's primary Agent model without configuring an API or runtime.\n\n### How is GPT-6 Astra different from GPT-5.6 Sol?\n\nBoth support Agent execution. Astra's main gains are in long-running tasks, computer use, software engineering, and complex tool coordination; results still depend on the task and system.\n\n### Does MuseWork use GPT-6 Astra?\n\nYes. GPT-6 Astra is the primary foundation model for the MuseWork Agent, strengthening complex-task understanding, multi-step execution, tool coordination, and professional delivery.\n\n## Sources\n\n- OpenAI, [GPT-6 Astra: A New Generation of Intelligence](https://openai.com/index/gpt-6-astra), September 3, 2026.\n- OpenAI, [GPT-6 Astra System Card](https://deploymentsafety.openai.com/gpt-6-astra), published September 3 and updated September 9, 2026.\n- ARC Prize, [OpenAI's GPT-6 Astra on ARC-AGI-3](https://arcprize.org/blog/astra), September 2026.\n- Artificial Analysis, [Benchmarking GPT-6 Astra](https://artificialanalysis.ai/articles/benchmarking-gpt-6-astra), September 9, 2026.\n- CNBC, [OpenAI announces rollout of GPT-6 Astra model](https://www.cnbc.com/2026/09/03/open-ai-astra-gpt-6-cyber.html), September 3, 2026.\n\n*Information checked September 23, 2026. MuseWork model integration is product information. Third-party benchmarks and safety findings reflect their publishers' test conditions and do not guarantee results for every task.*",[253,256,259,262,265],{"question":254,"answer":255},"What is GPT-6 Astra?","GPT-6 Astra is OpenAI's September 2026 frontier model focused on computer use, tool calling, software engineering, and long-running complex tasks.",{"question":257,"answer":258},"When was GPT-6 Astra released?","OpenAI released GPT-6 Astra on September 3, 2026. Availability can vary by product, region, and account.",{"question":260,"answer":261},"How can I use GPT-6 Astra?","Developers can follow OpenAI API, paid ChatGPT, and cloud-platform availability. Users can also access it as MuseWork's primary Agent model without configuring an API or runtime.",{"question":263,"answer":264},"How is GPT-6 Astra different from GPT-5.6 Sol?","Both support Agent execution. Astra's main gains are in long-running tasks, computer use, software engineering, and complex tool coordination; results still depend on the task and system.",{"question":266,"answer":267},"Does MuseWork use GPT-6 Astra?","Yes. GPT-6 Astra is the primary foundation model for the MuseWork Agent, strengthening complex-task understanding, multi-step execution, tool coordination, and professional delivery.",{"id":269,"title":270,"seo_title":271,"seo_keywords":272,"slug":279,"summary":280,"cover_url":281,"cover_alt":282,"author":13,"category":14,"tags":283,"is_pinned":22,"is_featured":23,"published_at":290,"modified_at":290,"content":291,"faq":292},"1790146979000-musework-across-every-screen","How to Continue MuseWork Tasks Across Desktop, Mobile, and Web","MuseWork Cross-Device Work: Continue Tasks on Desktop, Mobile & Web",[273,274,275,276,277,278],"MuseWork cross-device","sync tasks across devices","continue tasks on phone and computer","AI agent","cloud sandbox","local runtime","musework-across-every-screen","MuseWork keeps tasks, conversations, and deliverables in sync across desktop, mobile, and Web, with natural-language control over cloud and local execution.","/images/musework-across-every-screen-v2.webp","MuseWork synchronizing task progress, conversations, and deliverables across desktop, Web, and mobile",[284,285,286,287,288,289],"Cross-device work","Task sync","Desktop","Mobile","Cloud sandbox","Local environment",1790146979,"MuseWork lets you continue the same task across Web, Windows, macOS, Linux, iOS, and Android while keeping conversations, progress, and deliverables connected. You can also specify in natural language whether work should run in a cloud sandbox or on a connected computer.\n\n> **Every screen can be your MuseWork workspace.**\n\n## How MuseWork syncs tasks across desktop, mobile, and Web\n\nCross-device work means the work itself continues—not merely that the same chat appears on several screens.\n\n- **Conversation stays continuous.** Muse retains the goal, confirmed requirements, and current progress.\n- **Task status stays visible.** Active work, pending approvals, and completed results are available from the right device.\n- **Deliverables remain available.** Reports, presentations, spreadsheets, webpages, and code stay in one context.\n- **Preferences carry over.** Tone, structure, format, and collaboration boundaries do not need to be reset.\n\n| Access point | Best for | How work continues |\n|---|---|---|\n| Web | Starting quickly, uploading material, reviewing deliverables | Sign in and resume the current context |\n| Desktop | Authorized local files, folders, browsers, and tools | Run locally while synchronizing status |\n| Mobile | Progress, requirements, approvals, and results | Manage cloud or connected-computer work |\n\n## Web: start quickly and continue anywhere\n\nOpen a browser to describe work, upload material, review progress, and receive deliverables without installing an app. Instructions added on mobile and local material used on desktop stay connected to the same goal.\n\n## How MuseWork Desktop uses local files and tools\n\nRepositories, design assets, internal documents, datasets, and specialist tools often live on your computer. [Download MuseWork Desktop for Windows, macOS, or Linux](/en-US/download) so Muse can use the local environment within the scope you authorize.\n\nSay “inspect this project folder and run tests without uploading the whole repository,” or “run in the cloud sandbox and do not access local files.” You define the goal, location, and boundaries.\n\n## Cloud sandbox vs local computer\n\n| Task situation | Better environment |\n|---|---|\n| No local material; start quickly | Cloud sandbox |\n| Local projects, files, or tools are required | Desktop local environment |\n| An isolated experiment should not affect the computer | Cloud sandbox |\n| Results must be written to a local folder | Desktop local environment |\n\n## Continue MuseWork tasks on your phone\n\n[Download MuseWork for iOS or Android](/en-US/download) to start tasks, check progress, answer questions, add requirements, approve actions, and review results. Keep the connected desktop app running when a task still needs local files or tools.\n\n## Cross-device example: a quarterly review\n\n1. **Start on the Web.** Upload notes and data; define the audience and output.\n2. **Add local material on desktop.** Use an authorized folder, spreadsheet, and presentation template.\n3. **Check progress on mobile.** Review an anomaly and ask Muse to add it to the risk slide.\n4. **Finish on desktop.** Inspect the editable deck and export with the local template.\n5. **Return anywhere.** Revise or begin the next task from the same context.\n\n## Move from “which app?” to “how should this work be completed?”\n\nTell Muse the outcome first, then specify the environment, material, and boundaries. Research in the cloud, use authorized local files and tools, approve from mobile, and keep a deliverable you can continue anywhere.\n\n## Give Muse a task now\n\n> **[Continue in MuseWork on the Web](/en-US/chat?scene=cross-device-workflow&article_slug=musework-across-every-screen&locale=en-US&cta_position=final&cta_copy_id=continue_web&cta_destination=chat&task_scene=cross-device-workflow)**\n\n### Start in three steps\n\n1. **Choose an access point.** Use Web or [visit the MuseWork download page](/en-US/download).\n2. **Describe the task and environment.** Choose cloud or a connected computer.\n3. **Continue anywhere.** Check progress, add requirements, approve actions, and receive results.\n\n## Frequently asked questions\n\n### Which systems does MuseWork support?\n\nMuseWork supports Windows, macOS, Linux, iOS, Android, and the Web.\n\n### Do I need to install the Web app?\n\nNo. Use a browser; install Desktop only when a task needs local files or tools.\n\n### Can my phone continue a computer task?\n\nYes. Check progress, add requirements, approve actions, and receive results. Keep Desktop running if local resources are still needed.\n\n### Is the same task shared across devices?\n\nYes. Conversation, progress, deliverables, and preferences remain in one task context.\n\n### Can I choose cloud or local execution?\n\nYes. Choose a cloud sandbox or an authorized local environment and define its boundaries.\n\n### Does Muse access my entire computer?\n\nNo. Local access is limited to the scope you authorize and the boundaries in your request.",[293,296,299,302,305,308],{"question":294,"answer":295},"Which systems does MuseWork support?","MuseWork supports Windows, macOS, Linux, iOS, Android, and the Web.",{"question":297,"answer":298},"Do I need to install the Web app?","No. Use a browser; install Desktop only when a task needs local files or tools.",{"question":300,"answer":301},"Can my phone continue a computer task?","Yes. Check progress, add requirements, approve actions, and receive results. Keep Desktop running if local resources are still needed.",{"question":303,"answer":304},"Is the same task shared across devices?","Yes. Conversation, progress, deliverables, and preferences remain in one task context.",{"question":306,"answer":307},"Can I choose cloud or local execution?","Yes. Choose a cloud sandbox or an authorized local environment and define its boundaries.",{"question":309,"answer":310},"Does Muse access my entire computer?","No. Local access is limited to the scope you authorize and the boundaries in your request.",{"id":312,"title":313,"seo_title":314,"seo_keywords":315,"slug":321,"summary":322,"cover_url":323,"cover_alt":324,"cover_width":20,"cover_height":21,"author":13,"category":88,"tags":325,"is_pinned":22,"is_featured":23,"published_at":326,"modified_at":326,"content":327,"faq":328},"1790035200000-meta-muse-for-work","Meta Muse for Work: What It Can Do—and When You Need a Work Partner","Meta Muse for Work: What It Can Do in 2026 | MuseWork",[316,317,318,319,320],"Meta Muse for work","Meta Muse business use cases","can Meta Muse be used for work","AI agent for work","AI work partner","meta-muse-for-work","Can Meta Muse handle real work? Explore files, spreadsheets, presentations, connectors, approvals, background tasks, and when MuseWork fits a longer workflow.","/images/meta-muse-for-work.webp","Meta Muse for Work—Task or Workstream? with one task tile expanding into a continuous sequence and final work file.",[316,317,318],1790035200,"**Short answer:** yes, Meta Muse can help with work. Meta says its personal AI agent can browse websites, use connected services, work with files, create artifacts, track goals, and keep some tasks moving in the background. That makes it useful for research, scheduling, inbox work, forms, reminders, and defined deliverables.\n\nThe harder question is whether your job is one task or an ongoing workstream. If the work crosses research, project files, decisions, deliverables, review, and follow-up, you may want a product designed around that entire working relationship—not only the first handoff.\n\n\n\n## What Meta Muse can do for work\n\nMeta introduced Muse as a personal AI agent, not simply a chatbot. It runs in a dedicated cloud environment with a browser and can use services you choose to connect. Official documentation says it can read, summarize, create, and edit artifacts including documents, spreadsheets, PDFs, images, and code or web apps.\n\nFor work, that opens several practical uses:\n\n- researching companies, suppliers, events, or travel options;\n- summarizing a file and turning it into another document;\n- organizing email and calendar information;\n- filling out web forms or completing browser-based steps;\n- setting reminders and monitoring an ongoing goal;\n- preparing a basic report, tracker, or other artifact.\n\nMeta also says Muse can continue some work after you close the app and return when it needs input. That can be valuable for tasks built around monitoring, reminders, and follow-up.\n\n## How Meta Muse handles files, services, and actions\n\nMeta's Help Center makes the workflow more concrete than the launch headline. Muse can read, create, edit, and summarize documents, spreadsheets, PDFs, images, code, and web apps. Finished artifacts appear in the conversation and are saved to the Library. A single request can also combine steps—for example, read a source document and create a separate summary artifact.\n\nConnected services add another layer. Meta says a connector can be limited to retrieving information or, where supported, allowed to take actions. Email access, for example, can be used to search relevant messages without downloading the entire inbox unless the user asks for that. Calendar connectors may proactively surface schedule changes. The exact connectors and actions available can vary by account, platform, and rollout.\n\nThat distinction matters at work. Reading a project update is not the same as sending a reply; drafting a purchase recommendation is not the same as placing the order. A useful brief should say which sources the agent may read, what it may create, and which external actions must wait for approval.\n\n## Where a personal agent fits best\n\nMeta Muse is clearest when the task lives across personal accounts and everyday services. Consider a request such as:\n\n> Find three flight options that match my schedule, watch the price, and remind me before the fare changes.\n\nThe input is a personal goal. The work involves browsing, comparing, monitoring, and possibly taking an action with approval. That is close to the product's official center of gravity.\n\nA work task can look similar at first. You might ask an agent to research competitors, summarize meeting notes, or create a spreadsheet. The difference appears later: professional work usually has an audience, an internal standard, several source files, and a next step after the first output.\n\n## When the job becomes a workstream\n\nImagine you are preparing a quarterly product review. The job is not simply “make slides.” It may require you to:\n\n1. collect product metrics from several spreadsheets;\n2. compare the current quarter with the last one;\n3. connect changes to campaign, customer, and roadmap context;\n4. identify risks and open questions;\n5. build a concise presentation for leadership;\n6. revise the story after review;\n7. turn decisions into owners and follow-up tasks.\n\nThe presentation is only one deliverable inside a longer chain. The real value comes from keeping the evidence, decisions, formatting preferences, and next actions connected.\n\nThis is where an **AI work partner** becomes useful. MuseWork is a separate product for professionals who want to provide a goal, relevant material, and boundaries, receive a reviewable result, and keep the same work context available for what comes next. For a direct product-level breakdown, see [MuseWork vs Meta Muse](/en-US/compare/musework-vs-meta-muse).\n\n> **Try this task in MuseWork**  \n> “Review these campaign results, meeting notes, and planning files. Explain what changed, identify three decisions the team needs to make, and prepare a leadership-ready brief with sources and open questions.”  \n> **[Build the brief from my files  ](/en-US/chat?scene=meta-muse-work-brief&article_slug=meta-muse-for-work&locale=en-US&cta_position=mid_workflow&cta_copy_id=workflow_task&cta_destination=chat&task_scene=meta-muse-work-brief)**\n\n## How MuseWork carries one project through research, data, slides, and visuals\n\nConsider a market-entry proposal. It rarely belongs to one format:\n\n1. **Research the market.** Use [MuseWork Deep Research](/en-US/features/deep-research) to define the question, gather credible web sources, combine them with your own files, and produce a structured report with evidence and open questions.\n2. **Work through the numbers.** Ask MuseWork to organize survey results, pricing data, campaign performance, or forecasts in a spreadsheet; compare scenarios, surface anomalies, and make the assumptions easy to review.\n3. **Turn the findings into a presentation.** Use [MuseWork Slides](/en-US/features/ai-slides) or the [research-to-presentation workflow](/en-US/usecase/research-to-presentation) to shape the report into an editable deck for the audience and decision at hand.\n4. **Create the visuals the story needs.** Use [MuseWork Image Generator](/en-US/features/ai-image-generator) for concept images, campaign directions, product scenes, or supporting visual assets, then refine them against the same brief.\n\nThe value is not four disconnected generators. The research question, evidence, calculations, narrative, visual direction, and feedback stay part of the same job.\n\n> **[Start a research-to-presentation workflow](/en-US/usecase/research-to-presentation)**\n\n## Is Meta Muse enough for a complete work project?\n\nChoose based on the shape of the job.\n\n| If your task mainly involves… | Start by considering… |\n|---|---|\n| Personal scheduling, shopping, reservations, reminders, or services connected to daily life | Meta Muse |\n| A defined web task or one artifact from a clear request | Meta Muse or a general work agent |\n| Several work sources that must become a decision-ready result | An AI work partner such as MuseWork |\n| Repeated reporting, project follow-up, and preferences that should carry into the next task | A persistent work-partner workflow |\n| Sensitive actions such as sending, publishing, purchasing, or changing important data | A product with clear review and approval controls |\n\nMeta Muse can produce professional artifacts. The more useful distinction is what stays connected before and after each artifact is created: source material, decisions, feedback, and the next step.\n\n## What control do you have while Muse works?\n\nMeta documents several controls that are worth checking before you connect work accounts:\n\n- **Approval scope:** Depending on the action and settings, Muse may ask once, ask for each action, or receive broader permission for a site or connector.\n- **Task details:** You can inspect additional information before granting permission.\n- **Activity history:** The Activity log records actions and permissions chronologically.\n- **Upcoming work:** Scheduled tasks and reminders appear in an Upcoming view.\n- **Connector access:** Connectors can be disconnected, and their default permissions can be reviewed later.\n- **Irreversible actions:** Sending an email or completing another external action may not be reversible, so review the recipient, content, amount, or destination rather than approving by habit.\n\nMeta also says important permission checks operate outside the AI model and credentials are stored separately. Those protections reduce risk; they do not remove the need to review consequential actions and verify important outputs.\n\n## Four work scenarios to complete with MuseWork\n\nThe following scenarios describe work that can be completed in MuseWork, using the project's files, requirements, and review standards as shared context.\n\n### 1. Meeting preparation\n\n**Provide:** the agenda, recent email thread, prior notes, calendar context, and relevant files.  \n**Ask for:** a one-page brief with decisions already made, open questions, risks, and talking points.  \n**Review:** names, dates, commitments, and any claim drawn from an old message.\n\n### 2. Market or competitor research\n\n**Provide:** the decision you need to make, target market, known competitors, internal notes, and source requirements.  \n**Ask for:** a sourced report that separates verified facts, interpretations, unknowns, and recommended next steps.  \n**Review:** whether every important claim is supported and whether contradictory evidence was addressed.\n\n### 3. Spreadsheet analysis and management reporting\n\n**Provide:** budget and actuals, campaign exports, sales data, or operating metrics, plus the definitions that matter.  \n**Ask for:** cleaned tables, calculated changes, segment comparisons, anomalies, charts, and a short explanation of what moved and why.  \n**Review:** formulas, units, date ranges, missing values, and the assumptions behind any forecast.\n\n### 4. Research to presentation\n\n**Provide:** the research report, source files, audience, decision, required length, and any existing PPTX or template.  \n**Ask for:** a findings-led, editable presentation with a clear argument, supporting evidence, and next actions. If the story needs original visuals, add a visual brief for [MuseWork Image Generator](/en-US/features/ai-image-generator).  \n**Review:** every number, quotation, reference, and visual interpretation before sharing.\n\n## The bottom line\n\nMeta Muse can be used for work, especially when the task involves browsing, connected services, reminders, or a clearly defined artifact. If your work continues across research, files, decisions, deliverables, revisions, and follow-up, evaluate the product as a work relationship rather than a single task.\n\nMuseWork is built around that longer arc: bring the goal, material, and boundaries; receive work you can inspect, refine, use, and continue.\n\n> **One real task is enough to find out.**  \n> **[Hand off a task to MuseWork  ](/en-US/chat?scene=meta-muse-for-work-final&article_slug=meta-muse-for-work&locale=en-US&cta_position=final&cta_copy_id=final_real_task&cta_destination=chat&task_scene=meta-muse-for-work-final)**\n\n## Frequently asked questions\n\n### Can Meta Muse create documents and spreadsheets?\n\nYes. Meta's official Help Center says Muse can read, create, edit, and summarize artifacts including documents, spreadsheets, PDFs, images, code, and web apps. Exact availability can vary.\n\n### Is Meta Muse only for personal tasks?\n\nNo. It can perform tasks that overlap with work. Meta positions it primarily as a personal AI agent, so evaluate whether its available context, controls, and workflow match your professional use case.\n\n### What is the difference between an AI assistant and an AI work partner?\n\nAn assistant may answer questions or complete individual tasks. A work partner is designed around a longer chain: understanding context, completing a reviewable result, incorporating feedback, and continuing the next step.\n\n### Can Meta Muse work with spreadsheets and presentations?\n\nMeta says Muse can read, create, edit, and summarize spreadsheets and other artifacts. The exact format and editing experience can vary. If your workflow begins with several data files and ends with an editable presentation, compare the full path—data cleanup, calculations, narrative, deck editing, and review—not only whether a file can be generated.\n\n### Can Meta Muse use my email and calendar for work?\n\nIt can use supported connectors that you choose to enable. Meta says connectors may retrieve information and, where supported, take actions according to your permission settings. Check the available connector, account scope, and approval behavior before using it with work data.\n\n### Can Meta Muse keep working after I close the app?\n\nMeta says Muse can continue some goals and background tasks and can show scheduled work in the Upcoming view. Availability and behavior may vary by account, platform, and task.\n\n### Should I give an AI agent access to all my work tools at once?\n\nStart smaller. Connect only the source needed for one reversible task, keep external actions behind approval, inspect the result and activity history, and widen access only when the workflow proves useful.\n\n## Related reading\n\n- [What Is Meta Muse? Features, Pricing, Privacy, and Availability](/en-US/blog/what-is-meta-muse)\n- [Best Meta Muse Alternatives for Getting Work Done in 2026](/en-US/blog/best-meta-muse-alternatives)\n- [Meta Muse vs ChatGPT Work vs Claude Cowork](/en-US/blog/meta-muse-vs-chatgpt-work-vs-claude-cowork)\n- [MuseWork vs Meta Muse](/en-US/compare/musework-vs-meta-muse)\n\n## Sources\n\n- Meta, [Introducing Muse](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/), September 8, 2026.\n- Meta Help Center, [How to get started with Muse](https://www.meta.com/help/artificial-intelligence/1331373868832401/).\n- Meta Help Center, [How to use Muse with artifacts](https://www.meta.com/help/artificial-intelligence/2074655449783957/).\n- Meta Help Center, [How Muse works with Connectors](https://www.meta.com/help/artificial-intelligence/1687253048996149/).\n- Meta Help Center, [How Muse works with your guidance and approval](https://www.meta.com/help/artificial-intelligence/1385290430137537/).\n",[329,332,335,338,341,344,347],{"question":330,"answer":331},"Can Meta Muse create documents and spreadsheets?","Yes. Meta's official Help Center says Muse can read, create, edit, and summarize artifacts including documents, spreadsheets, PDFs, images, code, and web apps. Exact availability can vary.",{"question":333,"answer":334},"Is Meta Muse only for personal tasks?","No. It can perform tasks that overlap with work. Meta positions it primarily as a personal AI agent, so evaluate whether its available context, controls, and workflow match your professional use case.",{"question":336,"answer":337},"What is the difference between an AI assistant and an AI work partner?","An assistant may answer questions or complete individual tasks. A work partner is designed around a longer chain: understanding context, completing a reviewable result, incorporating feedback, and continuing the next step.",{"question":339,"answer":340},"Can Meta Muse work with spreadsheets and presentations?","Meta says Muse can read, create, edit, and summarize spreadsheets and other artifacts. The exact format and editing experience can vary. If your workflow begins with several data files and ends with an editable presentation, compare the full path—data cleanup, calculations, narrative, deck editing, and review—not only whether a file can be generated.",{"question":342,"answer":343},"Can Meta Muse use my email and calendar for work?","It can use supported connectors that you choose to enable. Meta says connectors may retrieve information and, where supported, take actions according to your permission settings. Check the available connector, account scope, and approval behavior before using it with work data.",{"question":345,"answer":346},"Can Meta Muse keep working after I close the app?","Meta says Muse can continue some goals and background tasks and can show scheduled work in the Upcoming view. Availability and behavior may vary by account, platform, and task.",{"question":348,"answer":349},"Should I give an AI agent access to all my work tools at once?","Start smaller. Connect only the source needed for one reversible task, keep external actions behind approval, inspect the result and activity history, and widen access only when the workflow proves useful.",{"id":351,"title":352,"seo_title":353,"seo_keywords":354,"slug":360,"summary":361,"cover_url":362,"cover_alt":363,"cover_width":20,"cover_height":21,"author":13,"category":88,"tags":364,"is_pinned":22,"is_featured":23,"published_at":365,"modified_at":326,"content":366,"faq":367},"1790035200001-best-meta-muse-alternatives","7 Best Meta Muse Alternatives for Getting Work Done in 2026","7 Best Meta Muse Alternatives for Work in 2026 | MuseWork",[355,356,357,358,359],"Meta Muse alternatives","best Meta Muse alternative","Meta Muse alternatives for work","AI agents like Meta Muse","AI agent for professional work","best-meta-muse-alternatives","Compare seven Meta Muse alternatives for research, spreadsheets, presentations, local files, recurring work, approvals, and finished deliverables.","/images/best-meta-muse-alternatives.webp","Editorial cover reading 7 Best Meta Muse Alternatives above the seven product marks for MuseWork, ChatGPT, Claude, Manus, Gemini, Genspark, and OpenClaw.",[355,356,357],1790035199,"Meta Muse is a capable personal AI agent. Meta says it can browse websites, connect to services, create files, track goals, and take actions with permission. But “an alternative to Meta Muse” can mean several different things.\n\nYou may need deeper project workflows, stronger local-file handling, a team environment, self-hosting, or broader access outside Meta Muse's current rollout. The right alternative depends on where the task happens, what result you need, and what should continue afterward—not on which product has the longest feature list.\n\n\n## When another tool may fit better\n\nMeta Muse is built around a personal agent that can work across connected services, files, and everyday goals. That is useful for many tasks, but some jobs begin elsewhere.\n\nA team preparing a weekly business review may need to work from shared project files and preserve the same definitions every week. A designer or analyst may need direct access to local folders. A company already centered on Google Workspace may prefer an agent that works inside Gmail, Drive, Docs, Sheets, and Meet. A technical team may want to host and configure the system itself.\n\nThe deliverable matters too. Booking an appointment is different from producing a sourced market report, an auditable spreadsheet, or an editable presentation. The alternatives below are grouped around those practical differences: where the work starts, what must be delivered, how actions are approved, and whether the project continues after the first result.\n## 1. MuseWork — for professional work that continues\n\n**Best for:** professionals whose work spans research, files, analysis, deliverables, review, and follow-up.\n\nMuseWork is positioned as an AI work partner. You provide a goal, relevant material, and boundaries. It is designed to handle the execution between those inputs and a result you can inspect, revise, and use.\n\nPresentations, documents, spreadsheets, pages, and visuals are only parts of the job. MuseWork connects them through the same context, decisions, standards, and follow-up, so the next step does not begin from zero.\n\n**Choose MuseWork when:**\n\n- your task crosses several files, tools, or stages;\n- the output must be ready for a manager, client, team, or personal decision;\n- research needs to become a report, presentation, plan, or follow-up;\n- you want the same partner to retain relevant context as the work continues.\n\n## 2. ChatGPT Work — for a broad AI work environment\n\n**Best for:** individuals and teams already using ChatGPT Projects, connected apps, and a broad set of work capabilities.\n\nOpenAI describes ChatGPT Work as an agent for longer, multi-step work. It can gather context from supported tools, files, and desktop apps, then create finished documents, spreadsheets, presentations, reports, and Sites. Existing project context can be used when Work starts inside a ChatGPT Project.\n\n**Choose ChatGPT Work when:** you want one general environment for research, analysis, creation, connected apps, and team context.\n\n**Check first:** availability depends on plan, platform, workspace settings, and administrator controls.\n\n## 3. Claude Cowork — for hands-on desktop and file work\n\n**Best for:** knowledge workers whose task lives in selected local folders, desktop tools, and browser workflows.\n\nAnthropic says Cowork lets users give Claude a goal, authorize folders and tools, and receive completed work for review. Official desktop guidance highlights research synthesis, file organization, document generation, spreadsheets, and presentations. File access is limited to folders the user connects.\n\n**Choose Claude Cowork when:** local files and transparent desktop steps are central to the task.\n\n**Check first:** Cowork uses more capacity than ordinary chat, and availability varies by plan and organization settings.\n\n## Meta Muse — the baseline for personal delegation\n\nThe original product may still be the most direct fit for some tasks. Meta Muse remains the clearest fit when the job is a personal goal spread across daily services: scheduling, shopping, travel, reminders, monitoring, email, or forms.\n\nMeta also documents artifact creation, connected services, background work, and approval controls. If those match your job and Muse is available to you, replacing it may add complexity without adding value.\n\n## 4. Manus — for broad one-off execution\n\n**Best for:** users who want to hand an agent a defined objective and receive a completed result across research, browser work, analysis, or content production.\n\nManus is an execution-focused cloud agent for substantial, defined assignments. Its official toolkit highlights research, browser work, data analysis, content production, websites, slides, images, and other multi-step outputs.\n\n**Choose Manus when:** you want to hand off a broad objective and receive a finished result from a cloud workspace.\n\n**Check first:** credit use, current plan access, export needs, and whether the task should become a reusable ongoing workflow rather than a one-time run.\n\n## 5. Gemini for Workspace — for work centered on Google\n\n**Best for:** organizations whose email, documents, spreadsheets, meetings, and files already live primarily in Google Workspace.\n\nGemini for Workspace works close to Gmail, Drive, Docs, Sheets, Slides, and Meet. That can reduce the time spent moving source material into a separate tool when the organization already keeps most work in Google.\n\n**Choose Gemini for Workspace when:** the team's documents, spreadsheets, meetings, and access policies are already centered on Google Workspace.\n\n**Check first:** which features are enabled for your Workspace edition, how administrator controls affect access, and whether the job must cross tools outside Google's ecosystem.\n\n## 6. Genspark — for an all-in-one AI workspace\n\n**Best for:** users who want research, content creation, data analysis, files, and repeatable agent workflows in one cloud workspace.\n\nGenspark describes Super Agent as a system that can run multi-step tasks from a single prompt, use live browsing and code, work with larger files, and save workflows as reusable skills. Its broader workspace also brings together slides, documents, spreadsheets, meetings, and team collaboration.\n\n**Choose Genspark when:** you want a broad, cloud-based workspace with several creation and execution surfaces.\n\n**Check first:** the breadth can be more than you need for a focused workflow, and credit use varies with task complexity.\n\n## 7. OpenClaw — for technical users who want control\n\n**Best for:** developers and technical operators who prefer to configure, run, and maintain an agent themselves.\n\nOpenClaw is an open-source, self-hosted agent framework for users who want to choose infrastructure, models, channels, tools, and deployment details. It is structurally different from signing up for a managed consumer or work agent.\n\n**Choose OpenClaw when:** control over hosting, configuration, integrations, and the operating environment is a core requirement.\n\n**Check first:** who will own installation, updates, secrets, monitoring, backups, incident response, and the reliability of every connected tool. The software may be free, but operating it is still work.\n\n## Comparison table: what changes in practice?\n\n| Product | Natural starting point | Typical result | Continuity and control to check |\n|---|---|---|---|\n| MuseWork | A goal plus files, notes, data, or authorized work context | Reports, spreadsheet analysis, editable presentations, visuals, plans, and follow-up | Whether the same project context and standards should carry into the next task |\n| ChatGPT Work | A task in ChatGPT, often inside a Project or connected-app context | Documents, spreadsheets, presentations, reports, Sites, and supported actions | Project sources, reusable templates, scheduled tasks, plan and admin availability |\n| Claude Cowork | A goal plus selected folders, desktop tools, browser, or connectors | Completed file and knowledge work for review | Folder modes, visible steps, schedules, plan and organization settings |\n| Meta Muse | A personal goal, uploaded artifact, connected service, or web task | Actions, reminders, research, artifacts, and ongoing goals | Connector scope, approval defaults, Activity log, region and platform access |\n| Manus | A defined objective in a cloud execution workspace | Broad one-time research, analysis, creative, or technical output | Credits, exports, source handling, and whether continuity is required |\n| Gemini for Workspace | Work already living in Google's productivity environment | Email, document, spreadsheet, meeting, and presentation assistance | Workspace edition, administrator settings, and cross-ecosystem needs |\n| Genspark | A request in an all-in-one cloud workspace | Research, files, slides, sheets, media, and reusable workflows | Credit use, workspace breadth, and fit for the specific task |\n| OpenClaw | A self-managed agent environment | Highly configurable tasks through chosen models and tools | Setup, security, maintenance, monitoring, and operational ownership |\n\n## Which Meta Muse alternative fits your job?\n\nThe strongest fit usually looks like this:\n\n- **“Handle this personal task across connected everyday services.”** Meta Muse is built around this pattern; MuseWork may also help, so compare the services, permissions, and result you need.\n- **“Use these work files and get this deliverable ready for review.”** Consider MuseWork or Claude Cowork.\n- **“Bring together my team's broader ChatGPT context and tools.”** Consider ChatGPT Work.\n- **“Execute this substantial one-off objective.”** Consider Manus.\n- **“Keep the work inside our Google environment.”** Consider Gemini for Workspace.\n- **“I want to operate and customize the agent stack myself.”** Consider OpenClaw.\n\nDifferences become clearer when the same real assignment, source material, constraints, and definition of done are used across the strongest candidates.\n\n> **A useful comparison task**  \n> “Use these customer interviews, campaign results, and meeting notes to produce a decision-ready growth brief. Show the evidence behind each conclusion, flag uncertainty, and prepare the next-step plan for review.”  \n> **[Run this workflow in MuseWork  ](/en-US/chat?scene=meta-muse-alternatives-growth-brief&article_slug=best-meta-muse-alternatives&locale=en-US&cta_position=mid_workflow&cta_copy_id=workflow_task&cta_destination=chat&task_scene=meta-muse-alternatives-growth-brief)**\n\n## Can one task move from research to a finished presentation?\n\nInstead of testing isolated prompts, try one connected assignment: use [MuseWork Deep Research](/en-US/features/deep-research) to build an evidence-backed report, organize the supporting figures in a spreadsheet, turn the conclusions into an editable [MuseWork presentation tools](/en-US/features/ai-slides), and create the supporting [MuseWork visual tools](/en-US/features/ai-image-generator). This reveals whether the product can preserve the same audience, evidence, assumptions, and feedback across the whole job.\n\n## Why continuity matters after the first deliverable\n\nWork rarely ends when a file is generated. A report leads to a presentation. The presentation leads to a decision. The decision creates owners, follow-up, and another round of evidence.\n\nMuseWork organizes that continuation around three practical needs:\n\n- **completion:** move from a request to work you can inspect and use;\n- **context:** work with the relevant files and tools instead of making you repeatedly copy information;\n- **continuity:** carry the project, feedback, and working standards into what happens next.\n\nMuseWork becomes especially relevant when a request grows into a longer chain of research, files, decisions, deliverables, review, and follow-up.\n\n## Frequently asked questions\n\n### What is the best Meta Muse alternative for work?\n\nIt depends on the work. MuseWork fits professional workflows that should move from context to a reviewable result and continue. ChatGPT Work fits a broad project and team environment. Claude Cowork fits hands-on work with selected desktop files and tools.\n\n### Is there a free Meta Muse alternative?\n\nFree access and usage limits change frequently. Check each official product's current plan page rather than choosing based on a static roundup.\n\n### Which option is best for local files?\n\nMuseWork Desktop and Claude Cowork both describe workflows involving authorized local folders or files. Compare the exact platform support, permissions, output requirements, and continuation you need.\n\n## Start with one real job\n\nA first trial can stay small: one reversible task, a clear result, and only the context required to complete it. The evidence and output will show whether broader access is worthwhile.\n\n> **[Give MuseWork one task to finish  ](/en-US/chat?scene=meta-muse-alternatives-final&article_slug=best-meta-muse-alternatives&locale=en-US&cta_position=final&cta_copy_id=final_real_task&cta_destination=chat&task_scene=meta-muse-alternatives-final)**\n\n### Which alternative is best for research and presentations?\n\nCompare the full workflow. MuseWork can turn scoped research and supplied files into a structured report, spreadsheet analysis, an editable presentation, and supporting visuals. ChatGPT Work and Claude Cowork also document research and presentation workflows. The best fit depends on source access, editing requirements, review controls, and whether the project must continue.\n\n### Which alternative is best for recurring work?\n\nLook for reusable project context, templates, scheduled tasks, connected sources, and a clear review process. Do not assume that “background work” automatically means the product will retain every instruction or project decision.\n\n### Which option gives more control over local files?\n\nClaude Cowork documents selected-folder access and multiple mount modes. OpenClaw offers infrastructure-level control for users willing to operate it. MuseWork Desktop can work with authorized local context. Compare the exact operating system support and permission model before connecting sensitive folders.\n\n### Do I need to replace Meta Muse completely?\n\nNot necessarily. Different agents can serve different tasks. The more important question is whether adding another product reduces work or creates another place where context, permissions, and files must be maintained.\n\n## Related reading\n\n- [What Is Meta Muse?](/en-US/blog/what-is-meta-muse)\n- [Meta Muse for Work](/en-US/blog/meta-muse-for-work)\n- [Meta Muse vs ChatGPT Work vs Claude Cowork](/en-US/blog/meta-muse-vs-chatgpt-work-vs-claude-cowork)\n- [MuseWork vs Meta Muse](/en-US/compare/musework-vs-meta-muse)\n\n## Sources\n\n- Meta, [Muse](https://ai.meta.com/muse/) and [Introducing Muse](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/).\n- OpenAI, [ChatGPT Work](https://openai.com/chatgpt-work/) and [ChatGPT Work and Codex](https://help.openai.com/en/articles/20001275-chatgpt-work-and-codex).\n- Anthropic, [Claude Cowork](https://www.anthropic.com/product/claude-cowork) and [Claude for work](https://www.anthropic.com/learn/claude-for-work).\n- MuseWork, [Home](https://museai.im/en-US/home) and [Desktop](https://museai.im/desktop).\n\n- Genspark, [Super Agent](https://www.genspark.ai/helpcenter/super-agent).\n- Manus, [AI Agent Toolkit](https://manus.im/tools).\n- Google, [AI Tools for Business](https://workspace.google.com/solutions/ai/).\n- OpenClaw, [official repository](https://github.com/openclaw/openclaw).\n",[368,371,374],{"question":369,"answer":370},"What is the best Meta Muse alternative for work?","It depends on the work. MuseWork fits professional workflows that should move from context to a reviewable result and continue. ChatGPT Work fits a broad project and team environment. Claude Cowork fits hands-on work with selected desktop files and tools.",{"question":372,"answer":373},"Is there a free Meta Muse alternative?","Free access and usage limits change frequently. Check each official product's current plan page rather than choosing based on a static roundup.",{"question":375,"answer":376},"Which option is best for local files?","MuseWork Desktop and Claude Cowork both describe workflows involving authorized local folders or files. Compare the exact platform support, permissions, output requirements, and continuation you need.",{"id":378,"title":379,"seo_title":380,"seo_keywords":381,"slug":387,"summary":388,"cover_url":389,"cover_alt":390,"cover_width":20,"cover_height":21,"author":13,"category":88,"tags":391,"is_pinned":22,"is_featured":23,"published_at":392,"modified_at":326,"content":393,"faq":394},"1790035200002-meta-muse-vs-chatgpt-work-vs-claude-cowork","Meta Muse vs ChatGPT Work vs Claude Cowork: Which One Actually Gets Work Done?","Meta Muse vs ChatGPT Work vs Claude Cowork (2026)",[382,383,384,385,386],"Meta Muse vs ChatGPT Work","Meta Muse vs Claude Cowork","ChatGPT Work vs Claude Cowork","best AI agent for work","AI work agent comparison","meta-muse-vs-chatgpt-work-vs-claude-cowork","Compare Meta Muse, ChatGPT Work, and Claude Cowork across files, connected apps, spreadsheets, presentations, approvals, schedules, and recurring work.","/images/meta-muse-vs-chatgpt-work-vs-claude-cowork.webp","Editorial comparison cover for Meta Muse, ChatGPT Work, and Claude Cowork, shown with their product marks.",[382,383,384],1790035198,"Meta Muse, ChatGPT Work, and Claude Cowork can all move beyond ordinary chat. Each can work through multiple steps, use external context, and produce something useful. The meaningful difference is not whether they are “agents.” It is what kind of work each product is organized around.\n\n- **Meta Muse** is centered on personal goals and actions across everyday services.\n- **ChatGPT Work** is a broad work environment built around projects, connected apps, and finished materials.\n- **Claude Cowork** emphasizes delegated knowledge work in selected files, desktop tools, and browser workflows.\n\nIf your main question is which one fits a professional workflow, compare the starting context, review process, and what happens after the first result—not only the model behind it.\n\n\n## At a glance\n\n| Dimension | Meta Muse | ChatGPT Work | Claude Cowork |\n|---|---|---|---|\n| Primary framing | Personal AI agent | Agent for longer work and finished deliverables | Agentic mode for multi-step knowledge work |\n| Natural starting point | A personal task or goal | A task inside ChatGPT, often with project or connected-app context | A goal plus selected folders, tools, or browser context |\n| Typical strengths | Everyday actions, monitoring, connected services, background goals | Research, analysis, broad project context, documents, sheets, slides, reports, Sites | Local-file and desktop workflows, research synthesis, file organization, document production |\n| Browser and tools | Dedicated cloud environment and connected services | Connected apps plus browser and desktop capabilities where supported | Browser, desktop tools, and authorized folders where supported |\n| Review and control | Permission requests vary by action, connector, and settings | Permissions and takeover depend on the app, task, plan, and surface | Shows task steps; access is limited to selected folders and enabled tools |\n| Best fit | Personal delegation across daily services | Broad professional work inside the ChatGPT environment | Hands-on knowledge work grounded in desktop files and tools |\n\n## Meta Muse: strongest when the task starts in everyday life\n\nMeta introduced Muse as a personal AI agent that can take actions, use a browser, connect to services, and keep goals moving. Official Help Center pages also say it can create and edit documents, spreadsheets, PDFs, images, code, and web apps.\n\nThat means Meta Muse is not limited to reminders and bookings. It can overlap with professional work. Its clearest fit, however, remains a task that begins with personal context and crosses everyday services: organize email, monitor a price, plan travel, schedule something, fill a form, or follow an ongoing goal.\n\n**Choose Meta Muse when:**\n\n- personal accounts and daily services hold the relevant context;\n- the desired result includes actions, monitoring, or reminders;\n- you want a conversational personal agent and it is available in your region;\n- its permission model matches the access you are comfortable granting.\n\n## ChatGPT Work: strongest as a broad project environment\n\nOpenAI describes ChatGPT Work as an agent for longer, more involved tasks. It can research and analyze information, work across supported apps and files, and create finished documents, spreadsheets, presentations, reports, and Sites. Work can use context from existing ChatGPT Projects, and supported desktop flows may access local files and apps with permission.\n\nIts strongest appeal is breadth. A user or team already working in ChatGPT can bring together project instructions, conversations, files, connected apps, and different output formats without moving to a separate product category.\n\n**Choose ChatGPT Work when:**\n\n- your team already organizes context in ChatGPT;\n- you need a general environment spanning research, analysis, creation, and connected apps;\n- shared projects and broad tool coverage matter;\n- your plan and workspace settings support the workflow you need.\n\n## Claude Cowork: strongest for delegated desktop knowledge work\n\nAnthropic positions Cowork around complete, multi-step tasks for non-technical knowledge workers. You give it a goal, point it at selected folders and tools, and receive work for review. Anthropic highlights local-file access, browser tasks, research synthesis, file organization, spreadsheets, and formatted presentations.\n\nIts file controls are concrete: official desktop documentation says reads and writes are limited to folders the user connects. That makes Cowork especially easy to understand when the work is already sitting on a computer in a defined project folder.\n\n**Choose Claude Cowork when:**\n\n- the job begins with local files and desktop applications;\n- you want the agent to show its steps and work within selected folders;\n- the task is research, analysis, document creation, or file organization;\n- you are comfortable with the plan and usage requirements for agentic work.\n\n## How files, context, and approvals differ\n\n| Question | Meta Muse | ChatGPT Work | Claude Cowork |\n|---|---|---|---|\n| Where can context come from? | Uploaded artifacts, web browsing, and supported connectors | Chats, Projects, uploaded files, supported connected apps, and desktop context where available | Selected folders, connected tools, browser context, and supported cloud sources |\n| What can it produce? | Documents, spreadsheets, PDFs, images, code, web apps, and actions in supported services | Documents, spreadsheets, presentations, reports, Sites, and supported app actions | Documents, spreadsheets, presentations, organized files, research outputs, and supported tool actions |\n| How can work continue? | Background goals, reminders, scheduled tasks, and ongoing personal context | Projects, saved sources, reusable templates, and scheduled or event-triggered tasks where supported | Scheduled tasks, cross-device continuation, folders, connectors, and recurring workflows |\n| What can the user review? | Task details, approval prompts, Activity log, Upcoming tasks, artifacts | Progress, questions, proposed changes, important-action approvals, files and final materials | Plan, visible steps, files and tools used, folder permissions, and completed output |\n| What varies? | Region, account, platform, connectors, actions, and permission settings | Plan, platform, workspace settings, administrator controls, apps and actions | Plan, organization settings, platform, connectors, folder mode and cloud/local execution |\n\nThe same label—“spreadsheet,” “scheduled task,” or “connected app”—can describe very different workflows. What matters is where the data lives, how access is granted, what can be reviewed, and how corrections carry forward.\n\n## The same assignment, three different starting points\n\nConsider this request:\n\n> Prepare a quarterly product review from performance spreadsheets, customer notes, roadmap changes, and the last leadership meeting. Show what changed, why it matters, and the decisions needed next.\n\nWith **Meta Muse**, the fit depends on whether those sources are available through its current artifacts, browser, and connected services, and whether you want the task managed inside a personal-agent relationship.\n\nWith **ChatGPT Work**, you might begin inside a Project containing the source files and instructions, then ask Work to research, analyze, and create the final materials.\n\nWith **Claude Cowork**, you might authorize a project folder and relevant tools, then let Cowork synthesize the files and produce the deliverable for review.\n\nFor a meaningful comparison, give each product the same sources, instructions, deadline, and definition of done, then compare the review-ready result.\n\n## Walking the quarterly review through each product\n\n### With Meta Muse\n\nYou would define the review goal, provide or upload the relevant artifacts, and connect any supported services that hold useful context. Muse can research, work with documents and spreadsheets, and create artifacts. Before using it for a real review, check whether the needed work accounts and sources are available, set approval preferences, and inspect the Activity log as the task progresses.\n\n### With ChatGPT Work\n\nYou might begin in a Project that already contains the previous review, reporting definitions, uploaded files, and standing instructions. Work can gather information from supported apps, analyze the spreadsheets, and create the report or presentation. Reusable templates can help keep recurring business reviews consistent. Plan, app, and administrator settings determine which actions are available.\n\n### With Claude Cowork\n\nYou might select the local folder containing exports, the current deck, and supporting notes, then authorize the browser or connected tools needed for research. Cowork shows its steps and can produce a document, spreadsheet, or presentation for review. Folder mode matters: read-only may suit analysis, while editing the actual workbook or deck requires a write-enabled scope.\n\nThese setup differences determine where the work begins, what access must be granted, and what the reviewer can inspect before approving the final deck.\n\n## When the work needs to continue: MuseWork\n\nMuseWork is a separate AI work product built around a different starting point: professional tasks that must move from scattered context to a reviewable result and then continue.\n\nThe product is designed around a continuous work relationship:\n\n1. understand the goal, material, and boundaries;\n2. work through the research, analysis, organization, and production;\n3. return a result the user can inspect and revise;\n4. preserve the project context and working standards that should matter next time;\n5. help continue the next step instead of treating the delivered file as the end.\n\nMuseWork brings completion, in-context collaboration, and continued work with the same Assistant into one workflow, so the project can move forward without restarting from a blank conversation.\n\n> **Try the comparison with your own work**  \n> “Use these project files to prepare a quarterly review. Cite the evidence behind each conclusion, flag missing information, build the presentation, and list the follow-up actions that need my approval.”  \n> **[Create my quarterly review  ](/en-US/chat?scene=meta-muse-comparison-quarterly-review&article_slug=meta-muse-vs-chatgpt-work-vs-claude-cowork&locale=en-US&cta_position=mid_quarterly_review&cta_copy_id=quarterly_review&cta_destination=chat&task_scene=meta-muse-comparison-quarterly-review)**\n\n### What that quarterly review could look like in MuseWork\n\n- **Deep Research:** verify market and competitor changes, preserve citations, and separate confirmed evidence from assumptions.\n- **Spreadsheet work:** combine performance data, calculate changes, compare segments, and flag anomalies or missing inputs.\n- **Slides:** turn the findings into an editable leadership deck with a clear decision narrative.\n- **Visuals:** create supporting diagrams, concept images, or campaign directions that match the same audience and brief.\n- **Follow-up:** carry review comments into the revised deck and turn approved decisions into owners and next actions.\n\nExplore the connected workflow through [MuseWork Deep Research](/en-US/features/deep-research), [MuseWork Slides](/en-US/features/ai-slides), [Research to Presentation](/en-US/usecase/research-to-presentation), and [MuseWork Image Generator](/en-US/features/ai-image-generator).\n\n## Which one should you choose?\n\n### Choose Meta Muse if…\n\nYour priority is personal delegation across everyday services, and the task includes actions, monitoring, reminders, or personal goals.\n\n### Choose ChatGPT Work if…\n\nYou want a broad professional environment connected to ChatGPT Projects, apps, research, analysis, and multiple finished formats.\n\n### Choose Claude Cowork if…\n\nYour work is grounded in selected local folders and desktop tools, and you want to delegate a complete knowledge-work task for review.\n\n### Choose MuseWork if…\n\nYou want the same work partner to understand your professional context, produce a reviewable result, absorb your feedback, and continue the work that follows.\n\n## Frequently asked questions\n\n### Is Meta Muse better than ChatGPT Work?\n\nThe better fit depends on where the task starts and what you need at the end. Meta Muse is designed around personal delegation and connected everyday services. ChatGPT Work emphasizes longer professional work, connected context, and finished deliverables.\n\n### Is Claude Cowork only for coding?\n\nNo. Anthropic positions Cowork for non-coding knowledge work such as research, analysis, document creation, file organization, spreadsheets, and presentations. Claude Code remains the coding-focused product.\n\n### Can all three create files?\n\nOfficial documentation indicates that all three can create or work with multiple kinds of files or deliverables. Exact formats and editing workflows vary by product, plan, and surface.\n\n### Which is best for recurring professional work?\n\nCheck how each product handles project context, scheduled or background work, connected sources, review, and reuse of instructions. The best choice depends on where your work lives and how much context should carry forward.\n\n### Which product is easier to audit while it works?\n\nMeta Muse offers task details, approval prompts, an Activity log, and an Upcoming view. ChatGPT Work lets users follow progress, answer questions, redirect work, and approve important actions. Claude Cowork shows the files, tools, and steps it uses and scopes local access to selected folders. The practical difference depends on the task and the controls available on your plan or workspace.\n\n### Can these products run recurring work?\n\nAll three describe forms of continued or scheduled work. Meta Muse supports ongoing goals, reminders, and scheduled tasks; ChatGPT Work supports scheduled or event-triggered tasks where available; Claude Cowork supports recurring schedules and cross-device continuation. Check whether a specific task depends on a local machine, connected app, administrator setting, or plan.\n\n### Which one should I use for spreadsheets?\n\nMeta Muse can create and edit spreadsheet artifacts. ChatGPT Work can create or edit spreadsheet files and, where supported, native Google Sheets. Claude Cowork can work with spreadsheets in selected folders or connected tools. Compare formula transparency, source access, output format, and how corrections carry into the next version.\n\n## Start with the result you need\n\nInstead of asking which agent is smartest, define the work: inputs, audience, required evidence, deliverable, review points, and next action. Then test the tools that fit those conditions.\n\n> **[Give MuseWork a real task  ](/en-US/chat?scene=meta-muse-comparison-final&article_slug=meta-muse-vs-chatgpt-work-vs-claude-cowork&locale=en-US&cta_position=final&cta_copy_id=final_real_task&cta_destination=chat&task_scene=meta-muse-comparison-final)**\n\n## Related reading\n\n- [What Is Meta Muse?](/en-US/blog/what-is-meta-muse)\n- [Meta Muse for Work](/en-US/blog/meta-muse-for-work)\n- [Best Meta Muse Alternatives for Work](/en-US/blog/best-meta-muse-alternatives)\n- [MuseWork vs Meta Muse](/en-US/compare/musework-vs-meta-muse)\n\n## Sources\n\n- Meta, [Muse](https://ai.meta.com/muse/), [Introducing Muse](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/), and official Muse Help Center documentation.\n- OpenAI, [ChatGPT Work](https://openai.com/chatgpt-work/), [ChatGPT Work and Codex](https://help.openai.com/en/articles/20001275-chatgpt-work-and-codex), and official file-creation documentation.\n- Anthropic, [Claude Cowork](https://www.anthropic.com/product/claude-cowork), [Claude for work](https://www.anthropic.com/learn/claude-for-work), and Claude Desktop documentation.\n- MuseWork, [Home](https://museai.im/en-US/home) and [Desktop](https://museai.im/desktop).\n",[395,398,401,404,407,410,413],{"question":396,"answer":397},"Is Meta Muse better than ChatGPT Work?","The better fit depends on where the task starts and what you need at the end. Meta Muse is designed around personal delegation and connected everyday services. ChatGPT Work emphasizes longer professional work, connected context, and finished deliverables.",{"question":399,"answer":400},"Is Claude Cowork only for coding?","No. Anthropic positions Cowork for non-coding knowledge work such as research, analysis, document creation, file organization, spreadsheets, and presentations. Claude Code remains the coding-focused product.",{"question":402,"answer":403},"Can all three create files?","Official documentation indicates that all three can create or work with multiple kinds of files or deliverables. Exact formats and editing workflows vary by product, plan, and surface.",{"question":405,"answer":406},"Which is best for recurring professional work?","Check how each product handles project context, scheduled or background work, connected sources, review, and reuse of instructions. The best choice depends on where your work lives and how much context should carry forward.",{"question":408,"answer":409},"Which product is easier to audit while it works?","Meta Muse offers task details, approval prompts, an Activity log, and an Upcoming view. ChatGPT Work lets users follow progress, answer questions, redirect work, and approve important actions. Claude Cowork shows the files, tools, and steps it uses and scopes local access to selected folders. The practical difference depends on the task and the controls available on your plan or workspace.",{"question":411,"answer":412},"Can these products run recurring work?","All three describe forms of continued or scheduled work. Meta Muse supports ongoing goals, reminders, and scheduled tasks; ChatGPT Work supports scheduled or event-triggered tasks where available; Claude Cowork supports recurring schedules and cross-device continuation. Check whether a specific task depends on a local machine, connected app, administrator setting, or plan.",{"question":414,"answer":415},"Which one should I use for spreadsheets?","Meta Muse can create and edit spreadsheet artifacts. ChatGPT Work can create or edit spreadsheet files and, where supported, native Google Sheets. Claude Cowork can work with spreadsheets in selected folders or connected tools. Compare formula transparency, source access, output format, and how corrections carry into the next version.",{"id":417,"title":418,"seo_title":419,"og_title":420,"seo_keywords":421,"slug":428,"summary":429,"cover_url":430,"cover_alt":431,"cover_width":432,"cover_height":433,"author":13,"category":33,"tags":434,"is_pinned":22,"is_featured":23,"published_at":435,"modified_at":435,"faq":436,"content":446},"1789862400000-jev-ai","What Is Jev? The AI Model That Doesn't Chat—And Why It Matters","What Is Jev? TypeSafe AI's System One Model Explained | MuseWork","Jev Doesn't Chat. So Why Is Everyone Talking About It?",[422,423,424,425,426,427],"Jev","Jev AI","TypeSafe AI","System One Model","Jev vs ChatGPT","Jev use cases","what-is-jev-ai","Jev does not chat or write. It returns fast, typed decisions with probabilities. Learn how TypeSafe AI's System One model works, what it can do, and what its headline claims really mean.","/images/jev-musework-final-cover.webp","MuseWork editorial cover introducing Jev as a System One model for typed decisions.",1136,640,[422,424,425],1789862400,[437,440,443],{"question":438,"answer":439},"Can Jev replace ChatGPT?","No. Jev makes bounded decisions; chat-oriented language models generate explanations and content. They are more likely to complement each other.",{"question":441,"answer":442},"Can Jev make mistakes?","Yes. Its output can match the required type and still contain the wrong decision.",{"question":444,"answer":445},"Can anyone use Jev?","It is currently aimed mainly at developers and software teams through an API and remains in early access.","Jev has become one of the most discussed AI launches of the week. The headlines are hard to miss: “up to 200× faster,” “up to 400× cheaper,” and even “no hallucinations.”\n\nBut Jev does not write emails, generate code, or hold a conversation. So what is it for?\n\nThe short answer:\n\n> **Jev is designed to make fast decisions within a set of choices, not to write an open-ended answer.**\n\nWant to act on what you have learned instead of opening a blank chat? Start MuseWork with a concrete Jev research task, or review the Deep Research workflow first.\n\n**[Try MuseWork with this task](https://museai.im/en-US/chat?scene=blog-jev-deep-research)** · [See how Muse Deep Research works](https://museai.im/en-US/features/deep-research?utm_source=blog&utm_medium=organic&utm_campaign=jev_explainer&utm_content=top_research)\n\nTypeSafe AI introduced Jev on September 15, 2026 as its first public “System One Model.” As of September 20, it remains in early access.\n\n## Jev in plain English\n\nImagine a company receives this message:\n\n“My card was charged twice. Please fix this as soon as possible.”\n\nA chatbot might write a helpful paragraph saying this sounds like a billing issue. Software would then need to parse that paragraph to decide what to do.\n\nJev works more like a triage desk. Given options defined in advance, it can return:\n\n- Department: billing\n- Urgency: high\n- Customer sentiment: frustrated\n- Probability that human review is needed: 82%\n\nThe software can use those values immediately. TypeSafe calls its three answer shapes Choice, Score, and Noul—roughly, choose an option, rate something on a scale, or estimate whether a statement is true. You do not need to remember the names. The important distinction is that **Jev handles bounded judgment questions, not open-ended creative work.**\n\n## Is Jev a faster ChatGPT?\n\nNot really. They are built for different jobs.\n\n| If you need to… | Better fit |\n|---|---|\n| Write an email, report, article, or code | A generative language model |\n| Explain and reason through an open question | A generative language model |\n| Route a ticket to one of several teams | A decision model such as Jev |\n| Score risk, urgency, or relevance | A decision model such as Jev |\n| Research, create, revise, and deliver a finished work product | A complete AI work product or agent |\n\nA language model is like a colleague who can read, think, and write. Jev is closer to a very fast dispatcher: it looks at the available information and decides which predefined path comes next.\n\n## What could Jev be used for?\n\nLikely uses include:\n\n- Routing customer requests\n- Scoring urgency or risk\n- Moderating content\n- Choosing which AI model should handle a request\n- Checking whether another AI output needs review\n- Ranking candidates from a known list\n\nThese small decisions can happen millions of times inside real products. Using a large reasoning model for every one of them can add latency and cost. Jev is interesting because it is purpose-built for that hidden layer.\n\n## What can't Jev do?\n\nJev does not write prose, code, or explanations. It is not a complete agent, and it cannot independently carry a complex project from research to delivery.\n\nTypeSafe's documentation also lists uneven areas in the current model: exact counting, arithmetic, date comparison, and questions that hide several layers of reasoning. Jev currently takes text-based state; images, audio, and video must be converted first. English is its primary training language, so teams should test other languages on their own data.\n\nMost importantly, a valid output can still be wrong. If the allowed departments are sales, support, and billing, Jev will not invent “legal”—but it can still select billing when support was correct.\n\n## Are the speed and cost claims real?\n\nTypeSafe reports end-to-end response times of roughly 70–500 milliseconds on its tests. In selected workflow evaluations, it reports gains as high as about 193.6× in speed and 444.6× in cost. Its published price is $0.042 per million input tokens, with no separate output-token charge.\n\nThose numbers are worth watching, but they are vendor-run results for decision-shaped tasks—not a promise that Jev is 200× faster at everything. A model returning one constrained choice is doing a narrower job than a model writing a complete response. TypeSafe also acknowledges that its evaluation design and test conditions may favor Jev.\n\nA more accurate conclusion is: **Jev shows substantial speed and cost potential for bounded decisions, but teams still need to test it on their own workloads.**\n\n## Does “no hallucinations” mean Jev is always right?\n\nNo. Here, the claim mainly refers to output shape. Jev cannot return a value outside the answer space you define. That can prevent malformed JSON, invented fields, or unparseable responses.\n\nIt does not prevent a semantically wrong decision.\n\n> **Type-safe output is not the same as a true answer.**\n\nHigh-impact actions still need business rules, fallback paths, and human review.\n\n## Why should non-developers care?\n\nMost people may never call Jev directly. They may encounter it as an invisible component inside support systems, automation products, or AI agents.\n\nIts larger idea is what matters: future AI products may not use one giant model for every step. A generative model can research and create, a decision model can route and score, ordinary code can enforce exact rules, and people can approve consequential actions.\n\nJev may never become the chatbot you open every morning. It could still become part of the machinery behind many AI products.\n\n## How should you evaluate Jev today?\n\nJev is worth watching and testing, but it is too early to treat launch benchmarks as settled evidence. Ask four questions:\n\n1. Is it accurate on your real data?\n2. Do its probabilities help separate clear cases from uncertain ones?\n3. What happens when it is wrong?\n4. Do the latency and cost savings justify integration and monitoring?\n\nStart with frequent, reversible decisions whose possible answers are already known. Do not begin with payments, deletion, or other high-impact actions.\n\n## Turn the Jev news into a decision-ready brief\n\nIf your team needs more than a news summary, give [Muse Deep Research](https://museai.im/en-US/features/deep-research) this task:\n\n> Research TypeSafe AI's Jev using official sources and credible independent analysis. Separate verified facts from vendor claims, explain suitable use cases and known limits, and produce a one-page decision brief with risks, open questions, and a practical pilot checklist for our business.\n\nMuseWork can carry the work from source gathering and fact-checking to a reviewable report you can refine or turn into a presentation.\n\n**[Try MuseWork with this task](https://museai.im/en-US/chat?scene=blog-jev-deep-research)** · [See how Muse Deep Research works](https://museai.im/en-US/features/deep-research?utm_source=blog&utm_medium=organic&utm_campaign=jev_explainer&utm_content=bottom_research)\n\n## FAQ\n\n### Can Jev replace ChatGPT?\nNo. Jev makes bounded decisions; chat-oriented language models generate explanations and content. They are more likely to complement each other.\n\n### Can Jev make mistakes?\nYes. Its output can match the required type and still contain the wrong decision.\n\n### Can anyone use Jev?\nIt is currently aimed mainly at developers and software teams through an API and remains in early access.\n\n## Sources\n\n- [TypeSafe AI launch post](https://typesafe.ai/blog/introducing-system-one-models-and-jev)\n- [TypeSafe System One documentation](https://docs.typesafe.ai/concepts/system-one)\n- [TypeSafe introduction](https://docs.typesafe.ai/introduction)\n- [Jev 1.13 known limitations](https://docs.typesafe.ai/model-jaggedness/jev-1.13)\n- [TypeSafe workflow evaluations](https://evals.typesafe.ai/)\n- [LangChain's Jev guide](https://www.langchain.com/blog/building-a-harness-with-jev)\n- [heise coverage](https://www.heise.de/en/news/AI-model-Jev-to-make-machines-decide-faster-11457071.html)\n\n*Updated September 20, 2026. Jev is an early-stage product; availability, pricing, and published results may change.*\n",{"id":448,"title":449,"seo_title":450,"seo_keywords":451,"slug":458,"summary":459,"cover_url":460,"author":13,"category":33,"tags":461,"is_pinned":23,"is_featured":23,"published_at":462,"modified_at":435,"faq":463,"content":482},"1789036800000-meta-muse","What Is Meta Muse? Features, Pricing, Privacy, and Availability","What Is Meta Muse? Features, Pricing, Privacy & Availability",[452,453,454,83,455,456,457],"what is Meta Muse","Meta Muse AI agent","Meta Muse pricing","Meta Muse privacy","how to use Meta Muse","Meta Muse vs MuseWork","what-is-meta-muse","What is Meta Muse? Learn what Meta's AI agent can do, its reported pricing, privacy controls, current availability, and how it differs from MuseWork.","/images/what-is-meta-muse-v2.webp",[90,276],1788969600,[464,467,470,473,476,479],{"question":465,"answer":466},"Is Meta Muse free?","Meta says it is free for most needs and offers subscriptions for more usage. Media reported $20 and $100 monthly launch plans, but Meta has not published a complete public pricing table. Verify current terms in the official product.",{"question":468,"answer":469},"Is Meta Muse available outside the United States?","Meta has confirmed a U.S. rollout but has not published a country-by-country expansion schedule.",{"question":471,"answer":472},"Can Meta Muse send emails or make purchases?","Meta says it can when the necessary service and permissions are available. Under the defaults Meta describes, Muse pauses before hard-to-reverse actions such as sending an email or making a purchase.",{"question":474,"answer":475},"Can Meta Muse see passwords or payment details?","Meta says the main agent does not see real credentials. They are stored separately and inserted for authorized actions. This is an architectural safeguard, not a guarantee against every security failure.",{"question":477,"answer":478},"Does Meta use Muse activity to train AI models?","Meta says sanitized interaction trajectories may be used for model training by default. Users can opt out in Muse settings.",{"question":480,"answer":481},"Is Meta Muse connected to MuseWork?","No. They are products from different companies and different teams. MuseWork is not affiliated with, endorsed by, or sponsored by Meta.","Meta Muse is a personal AI agent announced by Meta on September 8, 2026. Give it a goal and, with the right permissions, it is designed to use a browser and connected services to move the task forward—not just explain what you could do next.\n\nThat raises a practical set of questions. What can Meta Muse actually do? Is it free? Where is it available? What can it access? And is it the same product as MuseWork?\n\nHere is the short version:\n\n- Meta says Muse can browse websites, work with connected services, and continue some tasks after the app closes.\n- Under the defaults Meta describes, it pauses before hard-to-reverse actions such as sending an email or completing a purchase. Permission settings may vary.\n- Meta says Muse is free for most needs and offers subscriptions for additional usage, but it has not published a complete public pricing table.\n- Its current Secure VM separates credentials and permission controls from the main agent. Meta can still access data when needed to operate, secure, or support the service.\n- Meta says interaction “trajectories” may be used for model training by default. Users can opt out in Muse settings.\n- Meta Muse and MuseWork are unrelated products from different companies and teams.\n\nMost evidence available today comes from Meta's launch and technical material rather than long-term independent testing. Treat the capabilities below as Meta's stated product behavior unless noted otherwise.\n\n> **Looking for the right Muse?** Meta Muse is positioned as a personal agent for everyday tasks across consumer services. **MuseWork** is for professional work: bring a goal, brief, notes, research, or files and work toward a reviewable presentation, document, spreadsheet, visual, or code result. **[See what you can make with MuseWork](https://museai.im/en-US/home)**. The two products are not affiliated.\n\n## What is Meta Muse?\n\nMeta Muse is a consumer-focused personal AI agent powered by Meta's Muse Spark model family. It can be used through the Muse app, the web, or WhatsApp. Instead of stopping at an answer, Muse is intended to plan steps, use tools, and take actions on a user's behalf.\n\nEach Muse runs in a dedicated cloud environment called Muse Secure VM, with a browser, file system, storage, and task tools. Meta says credentials and permission decisions are separated from the main agent runtime.\n\nMuse and Muse Spark are not the same offering. Muse is the consumer agent; Muse Spark is the model family behind it. Model benchmarks and API prices do not describe the consumer app's subscription terms.\n\n## What can Meta Muse do?\n\n### Work through websites\n\nMeta says Muse can search the web, navigate sites, fill out forms, and complete transactions. Launch examples include travel booking, shopping, selling a car, negotiating a bill, and sending email. A user can watch the browser and take over when needed.\n\nThese examples show the intended range. They do not yet establish reliable performance across every unfamiliar website or complex workflow.\n\n### Use connected services with permission\n\nOfficial app listings mention email, calendar, Instagram, Facebook, health and fitness data, and financial information. Users choose which services to connect and what access to grant.\n\nAvailability may differ by service, platform, region, and account. Check the options shown in the product rather than assuming every connection is supported.\n\n### Continue longer-running tasks\n\nMeta says Muse can manage multiple tasks, continue some work after the app closes, and notify users when something changes or input is needed. A Goals view tracks longer-running objectives, while memory files and an activity trail show what Muse remembers, has done, and plans to do.\n\nFor important tasks, review that memory and activity trail instead of assuming every stored detail is still accurate.\n\n### Pause before consequential actions\n\nUnder Meta's described defaults, Muse stops before actions such as sending an email or making a purchase. A separate system called Sentinel evaluates connector actions and outbound requests, then allows, denies, or asks the user according to the applicable permissions.\n\nAt launch, Muse can check out through Link by Stripe. Meta says Link creates a one-time-use card so the merchant does not receive the user's regular card number. Planned support should not be treated as currently available until Meta confirms it.\n\n### Build tools for a task\n\nMeta says Muse can write code and create tools needed for a task. Public material does not fully document the review process, permissions, or limits, so this is best treated as an announced capability rather than a fully documented development platform.\n\n## Meta Muse pricing\n\nMeta says Muse is free for most needs, with subscriptions for more usage. The official materials reviewed for this article do not provide a complete public table of included usage, billing terms, or cancellation conditions.\n\nTechCrunch reported launch plans called Power at **$20 per month** and Maximum at **$100 per month**, as well as a payment-card requirement to get started. Those figures are media reporting, not pricing verified on a public Meta pricing page. Check the amount and terms shown in the official product before subscribing.\n\n## Meta Muse app and availability\n\nMeta offers Muse on iOS, Android, the web at `muse.ai`, and WhatsApp. Official app-store listings identify the developer as **Meta Platforms, Inc.** Check the developer name before downloading because many unrelated apps also use “Muse.”\n\nAs of this fact check, Meta's confirmed rollout is in the **United States**. A rollout does not guarantee immediate access for every account or device, and Meta has not published a country-by-country expansion schedule.\n\nMeta also says support for its AI glasses is coming, but has not announced a firm public date.\n\n## Meta Muse privacy: safeguards and tradeoffs\n\n### Credentials are separated from the main agent\n\nIn Muse Secure VM, credentials and authentication tokens are stored in an isolated component. Meta says the main agent receives substitute credentials for authorized actions instead of seeing the underlying secrets. Sentinel separately enforces connector and network permissions.\n\nThis reduces exposure; it does not eliminate risk. Meta says prompt injection remains an open problem and that Muse can still make mistakes.\n\n### Meta can access current Secure VM data when necessary\n\nThe launch version of Secure VM does not technically make user data inaccessible to Meta. Meta says staff access is restricted by policy, but the architecture does not prevent access when needed to support, secure, or operate the service.\n\nMeta has announced a stronger Muse Confidential VM for later in 2026. It is intended to use a user-held key to prevent Meta from accessing VM contents. It should be treated as a future capability until it is generally available.\n\n### Model training is opt-out\n\nMeta says conversations, tool calls, and related interaction trajectories may be sanitized and used to train future models by default. Users can opt out in Muse settings.\n\nMeta also says conversations and VM data are not shared with its advertising systems. Activity performed on third-party sites may still be treated by those services as the user's own activity.\n\nBefore connecting a sensitive personal or work account, review both the training setting and the permissions granted to that service.\n\n### Ready to use MuseWork for your next work task?\n\nAlready know you want to work from a goal or files toward a reviewable result? **[Log in to MuseWork and open your workspace](https://museai.im/en-US/login?returnTo=%2Fchat)**.\n\n## Meta Muse vs. MuseWork\n\nThe similar names can hide a straightforward difference in focus:\n\n- **Meta Muse:** a personal agent intended to carry out everyday tasks across websites and connected consumer services.\n- **MuseWork:** an AI work partner for turning goals and source material into professional deliverables that can be reviewed and refined.\n\nIf you need an agent to act across personal services, Meta Muse is the more relevant product to evaluate. If you need to turn a brief, notes, research, PDFs, or project files into a presentation, document, spreadsheet, visual, or code result, **[start with MuseWork](https://museai.im/en-US/home)**.\n\nMuseWork and Meta Muse are built by different companies and teams. There is no corporate affiliation, shared product team, endorsement, or sponsorship. For a fuller decision guide, read **[Meta Muse vs. MuseWork](https://museai.im/en-US/compare/musework-vs-meta-muse)**.\n\n## Frequently asked questions\n\n### Is Meta Muse free?\n\nMeta says it is free for most needs and offers subscriptions for more usage. Media reported $20 and $100 monthly launch plans, but Meta has not published a complete public pricing table. Verify current terms in the official product.\n\n### Is Meta Muse available outside the United States?\n\nMeta has confirmed a U.S. rollout but has not published a country-by-country expansion schedule.\n\n### Can Meta Muse send emails or make purchases?\n\nMeta says it can when the necessary service and permissions are available. Under the defaults Meta describes, Muse pauses before hard-to-reverse actions such as sending an email or making a purchase.\n\n### Can Meta Muse see passwords or payment details?\n\nMeta says the main agent does not see real credentials. They are stored separately and inserted for authorized actions. This is an architectural safeguard, not a guarantee against every security failure.\n\n### Does Meta use Muse activity to train AI models?\n\nMeta says sanitized interaction trajectories may be used for model training by default. Users can opt out in Muse settings.\n\n### Is Meta Muse connected to MuseWork?\n\nNo. They are products from different companies and different teams. MuseWork is not affiliated with, endorsed by, or sponsored by Meta.\n\n## The takeaway\n\nMeta Muse is notable because it moves the assistant experience from “tell me how” toward “help me do it.” Its browser, memory, background work, and connected services make that direction tangible. The open question is how consistently it can deliver that convenience while keeping permissions and approvals understandable.\n\nIf you try it, begin with limited access and a reversible task, then review its activity and settings. Pricing, connector coverage, international availability, and independent evidence of day-to-day reliability remain limited.\n\nIf your next task is professional rather than personal, skip the naming confusion and start from the work itself: **[bring your goal or files to MuseWork](https://museai.im/en-US/home)** and build a result you can inspect and refine.\n\n## Sources\n\n- Meta, [Muse: Meta's personal AI agent, features & capabilities](https://ai.meta.com/muse/), originally accessed September 10, 2026.\n- Meta, [Introducing Muse: The World's First Personal AI Agent Built for Everyone](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/), September 8, 2026.\n- Meta, [How We Designed Muse](https://introducing.muse.ai/), September 8, 2026.\n- Meta AI Research, [How We Built Safety Into Muse](https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse), September 8, 2026.\n- Meta AI Research, [Introducing Muse Spark 1.3](https://research.meta.ai/blog/introducing-muse-spark-1-3), September 2026.\n- TechCrunch, [Meta debuts its Muse AI agent. Will consumers trust it?](https://techcrunch.com/2026/09/08/meta-debuts-its-muse-ai-agent-will-consumers-trust-it/), September 8, 2026.\n- Apple App Store, [Muse from Meta](https://apps.apple.com/us/app/muse-from-meta/id6760173601), originally accessed September 10, 2026.\n- Google Play, [Muse from Meta](https://play.google.com/store/apps/details?id=com.facebook.aura), originally accessed September 10, 2026.\n",{"id":484,"title":485,"slug":486,"summary":487,"cover_url":488,"author":489,"category":490,"tags":491,"is_pinned":22,"is_featured":23,"published_at":492,"content":493},"17882451495516e68e572","Why MuseWork Is Putting Professional Execution First?","why-musework-is-putting-professional-execution-first","AI assistants are evolving in two directions: professional execution and everyday assistance. Here's why MuseWork is putting serious work first—while building memory and natural collaboration into the experience.","/images/muse-to-musework-1920x1080-optimized.webp","MuseWork Team","product",[],1788245511,"Over the past few months, we've noticed the AI assistant market splitting into two very different directions.\n\nProducts like Manus, Codex, and Genspark focus on professional execution. Their value is clear: give them a complex task, and they help you get serious work done.\n\nAnother category focuses on everyday assistance—managing calendars and emails, handling quick communication, and taking care of personal tasks. These products feel closer to personal assistants, though they usually place less emphasis on complex professional work.\n\nWhen we started building MuseWork, we wanted both.\n\nWe wanted it to handle serious work while also understanding you over time. We built long-term memory, continuous conversations, and even an “employment” relationship. Instead of creating a new chat for every task, you could keep working with the same assistant, much like working with the same person.\n\nThe hardest problem was organizing long-term context without overwhelming the model. We've now solved much of that at the system level.\n\nBut we also learned that these two directions create very different product demands.\n\nPeople using AI for professional work naturally want speed, parallel execution, project organization, and clear control over multiple tasks. People using AI for calendars, email, or everyday communication usually need a lighter and simpler experience, without the cost and complexity required for deep professional work.\n\nTrying to emphasize both equally made MuseWork harder to understand.\n\nThe value people recognize fastest is still straightforward: help me get meaningful work done.\n\nMemory, personal understanding, and natural collaboration matter deeply—but their value becomes clear only after the assistant has proven that it can reliably deliver.\n\nUsers know they are working with software. A manufactured personality alone doesn't create a genuine sense of collaboration. For us, the most meaningful form of “human touch” comes from remembering what matters, understanding how someone works, respecting their boundaries, and becoming easier to work with over time.\n\nWe've also been inspired by products like Town Assistant, which are exploring their own balance between professional execution and a more personal assistant experience.\n\nThis learning has shaped the latest version of MuseWork: stronger professional execution first, with memory and natural collaboration built into the experience.\n\nThe new version is live at [https://museai.im](https://museai.im/).\n\nMore free-credit rewards are coming next week. Try MuseWork with a real task—and tell us honestly where it still falls short.\n",{"id":495,"title":496,"slug":497,"summary":498,"cover_url":499,"author":489,"category":490,"tags":500,"is_pinned":22,"is_featured":23,"published_at":501,"content":502},"1781764665539b403647f","MuseWork: Exploring the Most Natural Way to Work with AI","musework-exploring-the-most-natural-way-to-work-with-ai","When Al becomes a long-term collaborator, what is the most natural way for humans and Al to work together? That is exactly what Muse is exploring.","/images/musework-exploring-the-most-natural-way-to-work-with-ai-v2.webp",[],1782126991,"Today, more and more AI Agent products are trying to execute complex tasks on behalf of users.\n\nThey can browse the web, call tools, generate files, and complete workflows — growing more capable by the day. But many still follow a \"task machine\" interaction paradigm: users create a task, add context, wait for the result, then manually check, organize, and archive it.\n\nThis is certainly a step beyond simple chatbots, but it still hasn't answered a more fundamental question:\n\n> **When AI truly becomes a long-term collaborator, what is the most natural way for humans and AI to work together?**\n\nThis is exactly what MuseWork is exploring.\n\nWe don't believe the answer is a more complex task system, nor that users should have to learn a new set of Agent operations. The most natural way to collaborate actually already exists — in how humans work remotely:\n\n> **It should feel like a real remote assistant you've never met in person.**\n\nWhen you have a remote colleague, assistant, or partner, you don't first think:\n\n- Should I start a new chat?\n- Should I create a new task?\n- Which project does this context belong to?\n- Do I need to re-explain the background?\n- Where should I file this deliverable?\n\nYou just talk to them like a person:\n\n- Let's keep pushing this forward.\n- The last version wasn't right — let's try a different direction.\n- Can you pull together all the related materials?\n- This is hard to explain over text — let's hop on a call.\n\n**This is the starting point of MuseWork's design.**\n\nOver the past two decades, IM, email, video calls, and collaborative docs have taught users how to work with another person remotely: send a message when something comes up, go back and forth for complex topics, jump on a call when text doesn't cut it — and the other person understands context, organizes materials, and keeps things moving forward.\n\n**MuseWork should not ask users to learn a new AI workflow.**\n\nIt should not require users to manually create chats, tasks, or projects just to fit the system's structure. Context management, task splitting, and file archiving — work that should be handled by the assistant — should not become the user's burden.\n\nThe \"new task\" in many Agent products and the \"new chat\" in many AI products are essentially management overhead. They appear to help users organize their work, but often just shift what the system can't automatically understand onto the user.\n\nIf you truly had a great assistant, projects shouldn't mainly be created by users — they should be proactively organized by the assistant.\n\nIt would recognize which conversations, files, deliverables, and decisions belong to the same project. It would periodically organize the Library according to real work threads. It would preserve versions, accumulate context, archive results, and automatically pick up where things left off the next time the user brings it up.\n\n> **MuseWork believes the user's most natural way of expressing themselves is the best interaction model.**\n\nYou don't need to write the perfect prompt, design a task structure, or manage context windows. You just need to say what you're thinking — like you would with a real human assistant.\n\n- Just say it when something comes up.\n- Hop on a call when text isn't enough.\n- After the meeting, MuseWork writes up the notes.\n- Once you confirm, MuseWork gets to work.\n\n**This is the direction MuseWork is exploring:**\nNot teaching users how to command an Agent, but teaching AI how to collaborate like a remote assistant.\n\nMuseWork's goal is not to make task execution more complex — it's to make collaboration more natural.\n\n**Great work** **together.**\n\n---\n",{"id":504,"title":505,"slug":506,"summary":507,"cover_url":508,"author":489,"category":509,"tags":510,"is_pinned":22,"is_featured":23,"published_at":511,"content":512},"17817651363264a431c85","From Agent Capability to Everyday Usability: The Design Principles Behind MuseWork","from-agent-capability-to-everyday-usability-the-design-principles-behind-musework","MuseWork's goal is not to showcase how complex Agent technology can be, but to make AI naturally usable for everyone.","/images/from-agent-capability-to-everyday-usability-the-design-principles-behind-musework-v2.webp","tech",[],1782126989,"**MuseWork's goal is not to showcase how complex Agent technology can be, but to make AI naturally usable for everyone.**\n\nOver the past year, products like OpenClaw and Hermes Agent have proven that Agents can write code, operate browsers, call tools, manage files, and accumulate skills and memory through long-term collaboration. These explorations are significant — they've pushed the boundaries of capability forward considerably.\n\nBut for everyday users, the first time they open an Agent product, what they see is often not capability but barriers: API keys, model selection, permission configuration, plugin installation, workflows, tokens, VMs.\n\n> **Users get blocked by AI before they even start using it.**\n\nThe question MuseWork wants to answer is: can someone who doesn't know how to deploy services, write prompts, understand models, or study Agents still have an AI assistant that's genuinely useful?\n\nWe believe the answer is yes. Whether you know how to use AI shouldn't determine whether you can benefit from it.\n\n## I. Zero Configuration: Conversation Is the Entry Point\n\nMuseWork doesn't put configuration in front of users. The first time you open MuseWork, you just state what you need.\n\nBehind the scenes, the system handles Agent initialization, workspace allocation, skill loading, long-term memory preparation, and session routing. Users don't need to create an Agent, select a model, install plugins, or understand context windows.\n\n> **Before users send their first message, they should not be asked to understand any system concepts.**\n\nConversation itself is the entry point. You send a message — that's the start. You keep talking — that's adding context. You switch topics — the system decides whether to open a new context. You return to a previous task — MuseWork picks up the existing context.\n\nBehind this are session parsing and multi-channel orchestration, but that complexity should not be exposed to users. Users shouldn't think \"Am I in the right session?\" — they should only think \"What do I want MuseWork to do?\"\n\n## II. Natural Interaction: Allow Interruptions, Additions, and Jumps\n\nReal conversations don't follow strict queues. Users might add \"include a chart\" mid-task, say \"stop,\" or suddenly ask an unrelated question.\n\nMuseWork determines whether a message cancels the current task, supplements the current request, queues a new task, or is independent conversation. State management is a system concern — it shouldn't become a user burden.\n\nSimilarly, users shouldn't have to re-explain themselves just because they switched channels. Web, Telegram, WeChat are just different ways of talking — behind them should be the same Agent, the same skills, the same memory.\n\n> **Channels shouldn't become different \"people.\" MuseWork is still MuseWork.**\n\n## III. Cost: Let Users Feel Free to Talk More\n\nIf users have to calculate cost with every message, AI can hardly become a daily tool. A good assistant should allow users to iterate, revise, and ask casually.\n\nCost optimization is therefore not just a business concern — it's an experience concern.\n\nMuseWork focuses on three things:\n\n- **Cache stable content.** System prompts, tool definitions, and skill descriptions are kept in stable order so the model doesn't repeatedly process what it has already read.\n- **Compress context in layers.** Images, tool returns, web content, logs, and long conversations are distilled into what's actually needed for continued collaboration.\n- **Wake execution environments on demand.** Regular conversation stays lightweight; heavier execution environments are only spun up when code needs to run, files need processing, or deliverables need generating.\n\n**Don't make users pay for the system's redundant work.**\n\n## IV. Privacy: You Can't Rely on a Promise Alone\n\nWhen users hand over files, accounts, calendars, email, and long-term preferences to AI, privacy concerns are legitimate. Truly reliable privacy design doesn't ask users to trust a promise — it makes things that shouldn't be accessed hard to access at the system level.\n\nMuseWork defaults to isolation: each user has an independent workspace; files are stored under the user's own cloud storage path; execution environments only access authorized directories; long-term memory is injected by the server on demand rather than scattered across execution environments.\n\nThird-party authorization tokens are encrypted. Access paths include identity verification, ownership verification, expiration controls, rate limits, and audit logs. Temporary file links require ownership confirmation before issuance and automatically expire.\n\n> **Privacy protection is not a marketing label — it's the default posture of system design.**\n\n## V. Memory: A Good Assistant Shouldn't Have to Relearn You Every Time\n\nOne of the most exhausting aspects of many AI products is that users have to re-introduce themselves every time: who they are, what they're working on, what format they prefer, where they left off.\n\nMuseWork watches for long-term useful information during conversations: project background, expression preferences, key constraints, milestone plans, user feedback. These aren't simply stuffed into chat history — they're recorded as different types of signals with confidence scores.\n\nMemory isn't just searching chat history either. MuseWork injects stable cognition before a conversation begins, so collaboration doesn't start from zero every time.\n\nAn asynchronous memory engine processes recent signals: which are more certain, which are outdated, which need merging, which should be forgotten. Internally, we call this process **\"Dreamweaving.\"**\n\n> **Every collaboration should make the next one a little easier.**\n\n## Conclusion\n\nMuseWork's principles are simple: **keep complexity in the system, keep simplicity for the user.**\n\nZero configuration, because setup shouldn't filter out users. Implicit sessions, because users shouldn't manage context. Unified multi-channel, because wherever users speak, they should be talking to the same assistant. Caching, compression, and on-demand execution, so users feel free to talk more. Isolation and encryption, to minimize what users must \"take our word for.\" Memory and Dreamweaving, to make long-term collaboration continuous.\n\n**The future of AI should not belong only to those who are best at using AI.**\n\nIt should also belong to those who simply want to get one thing done well, express one idea clearly, or make one day a little easier. That is the direction of MuseWork.\n",23,1,24,1792589010425]