Wandering Nomad

Free Virtual Assistant Client Onboarding Questionnaire + AI Follow-Up

2026-11-06T20:49:00.000+08:00

A virtual assistant client onboarding questionnaire gets a new client's priorities, tools and boundaries in writing before your first day. Below is the full question list, ready to paste into a Google Form, plus an AI step that turns the answers into a follow-up email and a first-week plan.

Onboarding just means the setup period at the start of a new client relationship. Get it right, and the first week feels calm instead of chaotic.

Why Ask Before You Start

Most VA roles are now part-time and mixed. In VA Masters' study of 323 VA roles from its own database, opened from February to August 2026, 72% were part-time, and 46% were opened at exactly 80 hours a month, roughly 20 hours a week. Only 26% of roles were single-task.

That's one recruitment agency's data, but the lesson is useful. If you only have 20 hours a week across two or three kinds of work, you can't spend the first five figuring out what the client wants.

A questionnaire gets the basics in writing. It also makes you look organized from day one, which is half of what a new client is judging.

The Virtual Assistant Client Onboarding Questionnaire

Copy these questions into a Google Form or a doc. Delete what doesn't fit your services.

[MAYE: paste your Google Form "make a copy" link here]

Section 1: About you and your business

Your name, business name and website.
In one or two sentences, what does your business do, and who are your customers?
What made you decide to hire a VA now?
What would make the first 30 days a success for you?

Section 2: Scope and priorities

List the tasks you want me to take over, starting with the most important.
Which of these is urgent this week?
Is there anything you never want me to handle or touch?
Roughly how many hours a week or month have we agreed on?
If I run out of hours, what should I do first: pause, ask, or finish the top priority?

Section 3: Communication

How do you prefer to communicate (email, Slack, WhatsApp, other)?
How fast do you expect a reply during my working hours?
Do you want a regular update? If yes, how often and in what format?
Do you prefer a weekly call, or messages only?
Who else on your team will I work with, and what do they do?

Section 4: Tools and access

Which tools do you use (email, calendar, task board, CRM, cloud storage, social media)?
Which accounts will I need access to?
How will you share access? (Please don't type any passwords into this form. A password manager or separate user accounts are safest.)
Are there any files, SOPs or past examples I should read first?

Section 5: Schedule and availability

What time zone are you in?
What are your usual working hours?
When do you need me online, if at all?
Any holidays or busy seasons coming up?

Section 6: Voice and preferences

How should I sound when I write on your behalf: formal, friendly, short?
Share one or two emails or posts that sound like you.
Any words, phrases or topics I should avoid?
How do you like to give feedback on my work?

Section 7: AI and tools

Are you OK with me using AI tools (like ChatGPT or Claude) to draft or research? For which tasks?
Is there any client or business information that must never go into an AI tool?
Do you have AI tools you'd like me to use, or that your company already pays for?
How should I mark work that AI helped draft, if at all?

Section 8: Admin and billing

Who should invoices go to, and how often?
How will you track my hours or tasks?
Is there a contract or NDA (non-disclosure agreement) to sign before I start?

Section 9: Anything else

Anything about how you work that a new VA usually gets wrong?
Anything else you'd like me to know?

How to Build It as a Google Form

Open Google Forms, start a blank form, and add each question. Use "Paragraph" for open answers and "Multiple choice" for things like preferred channel or reply speed. Section breaks keep it from feeling like a wall of questions.

Google's Forms help page explains how to store responses in a linked Google Sheet, so each client's answers land in one place.

Prefer Notion? Paste the list into a Notion page and send the client a copy, or use it as your own call script.

Send it a few days before your kickoff call, and tell the client it's fine to skip anything that doesn't apply. Then the call can be about the gaps, not the basics.

The AI Follow-Up Step

When the answers come in, don't just file them. Paste them into ChatGPT or Claude (minus anything sensitive) with this prompt:

"Here are a new client's onboarding answers. 1) Summarize their top three priorities in plain words. 2) List any answers that are vague, missing or contradict each other. 3) Draft a short, friendly follow-up email that thanks them and asks only about those gaps. 4) Suggest a first-week plan that fits [X] hours. Don't invent details that aren't in the answers. Answers: [paste]."

Then read everything before it goes anywhere. Check that the priorities match what the client actually wrote, and that the email sounds like you.

The gap list is the real win. Say the client named social media as a top priority but listed no social accounts under tools. That goes on the list. It catches the "we'll figure it out later" answers before they turn into a messy week three.

What This Means for VAs

A questionnaire is quiet proof that you run things like a business. Clients notice when they don't have to explain the same thing twice.

Section 7 matters more than it looks. It opens the AI conversation early, before a client assumes AI makes everything instant. My post on setting AI expectations with clients has scripts for that talk.

And once you know the tasks, write them down as SOPs with my free VA SOP template.

Make your own copy of the questionnaire and send it to your next new client before your kickoff call. You'll walk in already knowing what matters to them.

Virtual Assistant Skills for Your Resume: The 2026 List

2026-11-05T20:29:00.000+08:00

Only 1.2% of LinkedIn members list an AI skill. That makes AI the easiest way to help the virtual assistant skills on your resume stand out in 2026.

Below is the list I'd use, grouped into core, AI and people skills, plus a skills section you can copy.

The same LinkedIn report says members who list AI literacy skills are 78% more likely to get hired. AI literacy just means knowing how to use AI tools well, and knowing when not to trust them. Most people never write it down. You can.

Core Virtual Assistant Skills for Your Resume

AI sits on top of the basics. It doesn't replace them. Upwork's In-Demand Skills 2026 report ranks the top customer service and admin skills, starting with General Virtual Assistance, Data Entry, Digital Project Management, General Research Services and Executive Virtual Assistance.

Use those exact phrases where they're true for you. Clients and hiring software both search for the words they know.

Range matters too. The recruitment agency VA Masters looked at 323 roles from its own database and found only 26% covered a single type of work. The average role spanned two areas. So show a mix: admin plus research, or inbox plus social media.

AI Skills for Your Resume in 2026

"Knows ChatGPT" is too vague to help you. Name the task, the tool and the check you do.

Here's how I'd word them:

AI-assisted research, with every fact checked against the original source
Drafting emails, reports and social posts with ChatGPT or Claude, edited for the client's voice
Summarizing meetings and documents with AI
Workflow automation with Zapier (connecting apps so routine tasks run on their own)
Reviewing AI output for accuracy before it reaches the client

That last one is the line I'd put first, because AI can sound sure of itself and still be wrong.

Demand is real but still early. In a separate VA Masters report, 21.8% of admin and operations VA roles required AI, roughly 1 in 5. I break down which tools those posts ask for in which AI skills VA job posts actually ask for.

Soft Skills, Written So They Count

Every resume says "good communicator." Prove it in a short line instead.

Swap "detail-oriented" for "caught and fixed double-booked meetings before the client saw them." Swap "reliable" for "replied to client messages within two hours on workdays." Only write results you can back up, because a client may ask about any of them.

Skills for a Virtual Assistant With No Experience

No clients yet is fine. Pull skills from what you've already done: school, a BPO or office job, a family business, volunteering.

Running a group project is project management. Keeping a small shop's records is data entry and bookkeeping basics. Answering customer chats is client communication. Write the skill, then a few words of proof in brackets.

A Skills Section You Can Copy

This is a template, not my own resume. Keep only the lines that are true for you.

[MAYE: paste your Google Doc "make a copy" link here]

Core skills: Inbox and calendar management · Data entry and spreadsheet cleanup (Google Sheets, Excel) · Online research and reporting · Digital project management (Trello, Asana) · Travel and meeting coordination

AI and tools: AI-assisted drafting and research (ChatGPT, Claude) with source checking · Meeting and document summaries · Workflow automation (Zapier) · Canva · Google Workspace · Microsoft 365

People skills: Clear written communication · Handling confidential information · Time-zone coordination · Proactive problem-solving

Put this section near the top of your resume, under a two-line summary. Then make your Upwork profile match it. My 3 Upwork profile overview samples use the same wording.

What This Means for VAs

Your resume, your profile and your interview answers should tell one story. If your resume says "AI-assisted research with source checking," expect a client to ask how you check. My virtual assistant interview questions post has a sample answer for exactly that.

If you're still building these skills, start with how to become an AI virtual assistant, then come back and add each one as you learn it.

I share what I'm learning about AI for VAs every week on Instagram at @huneeroyo. Come say hi there.

How to Turn a Messy Spreadsheet Into a Claude Dashboard

2026-11-04T20:06:00.000+08:00

You can upload a messy spreadsheet to Claude, ask it to clean the data, and get back a dashboard with charts you can click through and share, all in one chat. No formulas, no coding, and it works on the free plan, though sharing a link needs a paid plan.

If you've ever been handed a client's "tracker" with merged cells, three date formats and a tab called FINAL_v2, this one's for you. Here's how to get from that sheet to a Claude dashboard, step by step, plus the checks I'd never skip.

To test it, I made a practice sheet that looks like a realtor client's listings research: 312 rows of made-up home listings with duplicates, mixed date styles, misspelled neighbourhoods, missing prices and a stray "average" row in the middle. No real client data went in. I ran everything in a normal Claude chat on my Max plan.

What Claude Can Do With a Spreadsheet

Two Claude features do the work here, and both are in Anthropic's help center.

The first is file creation and analysis. Claude runs code in a safe, closed-off space on Anthropic's side, so it can read the spreadsheet you upload, clean it, and give you back a new Excel file. You can upload CSV, TSV and other data files, up to 30 MB each. Anthropic lists it for every plan: Free, Pro, Max, Team and Enterprise.

The second is artifacts. An artifact is something Claude makes that opens in its own window beside the chat, like a document, a deck, or a dashboard. You can keep editing it and share it. Creating artifacts in a chat is also available on every plan.

One setting to check first. Artifacts and file creation both need the "Code execution and file creation" switch turned on. In newer versions of Settings, it's called "Create and edit files". On Free, Pro and Max, you'll find it under Settings, then Capabilities.

Claude Dashboard Examples: The Step-by-Step

1. Make a safe copy. Before anything touches AI, save a copy of the sheet and strip out what doesn't need to be there: customer names, emails, phone numbers. A dashboard about listings by month doesn't need anyone's personal details.

2. Ask Claude to describe the mess first. Upload the file and ask: "Before you change anything, list every problem you see in this data: duplicates, mixed date formats, blank rows, typos in category names." For my listings sheet, I swapped the last part for "typos in neighbourhood and property type names." This gives you a to-do list you can check, instead of fixes you can't see.

[ADD IMAGE: claude-problem-list.jpg]

Claude found every problem I'd hidden in the sheet: 6 duplicate listings, 5 blank rows, the stray average row, 18 missing prices and 5 prices with an extra zero. It even noticed the sheet mixed "Harbor View" and "Harbour View". Then it asked me three questions before touching anything, like whether "Single Family" should count as "House".

3. Clean it, and keep the receipt. Ask Claude to fix the problems and give you a cleaned Excel file, plus a short note on every change it made. You want the note, because your client will ask why a number moved.

In my test, the cleaned file came back with a change log sheet listing every fix, and the original file stayed untouched. Its average price, $489,704 across 277 listings, matched my own check to the dollar.

4. Ask for the dashboard with real questions. This is the step most people rush. Don't just say "make a chart." Say what the client wants to know. Here's what I asked: "Build an interactive dashboard from the cleaned data that shows new listings by month, the average list price by neighbourhood, the top five neighbourhoods by number of listings, and which neighbourhood's prices dropped since last quarter, with a filter by property type."

Anthropic's own tip for its dashboard feature says the same thing: say what you want to know, not just what to chart.

It took two follow-up messages. First, Claude showed me its plan and waited for my go-ahead before building anything. Then the "change since last quarter" column came out cut off on the right edge, so I asked it to fix that. The dashboard opened in its own window beside the chat, and the property type filter updated every chart on the page.

5. Check the numbers against the sheet. Pick three figures on the dashboard and find them in the original spreadsheet yourself. If the total for March on the dashboard doesn't match a simple sum in Excel, ask Claude to explain how it got there.

Claude told me straight out that it hadn't opened the dashboard to look at it, and asked me to check it over. Good thing I did. Harbor View showed a 29.6% price drop since last quarter, and Claude's summary said to treat that number with care. So I clicked the House filter, and the drop shrank to 10.4%. Harbor View simply had mostly houses listed from April to June, and mostly condos from July to September. If I'd sent the first number to a realtor, it would have looked like prices crashed.

I asked Claude to add one line under the table explaining the difference, and it did, with the numbers to back it up.

6. Share it. Artifacts start private. On Pro and Max, you can switch one to "Anyone with the link" or invite people by email. On Team and Enterprise, your client's admin decides whether links can go outside the company. Check who can see it before you send it, the same way you'd check a Google Drive link.

What About Claude's Dashboards Template?

You might see a "Dashboards" template in the Claude app. It's a different tool. It's in beta on paid plans (Pro, Max, Team and Enterprise), and it's built for data that lives in a company database or in a connected app like Salesforce. Claude writes the queries and each chart shows how it got its number.

For a spreadsheet someone emailed you, the regular chat steps above are what you need. If your client's data already lives in one of those systems, the template is worth a look.

What This Means for VAs

Reporting is one of the most useful things a VA can offer, and one of the most tedious to build by hand. With Claude, the cleaning and charting become a conversation.

But the value isn't the chart. It's that you asked the right questions, removed private data before uploading, and checked the totals before your client saw them. That's what makes a dashboard trustworthy, and it's still your job.

Start with a sheet you already know well, so you can spot a wrong number straight away.

If you build one, tell me what your client said about it.

Claude for Virtual Assistants: A Beginner's Guide From a VA

2026-11-03T19:33:00.000+08:00

New to Claude? Here's my quick answer on Claude for virtual assistants: start with plain chat, move each client into a Project, and leave Claude Code for later. I use Claude every day in my VA work, and that's the order I'd recommend to any VA starting out.

Here's what each part does, which one fits which task, and how to keep client data safe while you learn.

What Claude Is, in Plain Words

Claude is an AI assistant made by a company called Anthropic. It runs on a large language model, which is the AI brain behind tools like Claude and ChatGPT. You ask in normal words, and it writes, summarises or plans.

There's a free plan. Pro costs $17 a month if you pay yearly, or $20 if you pay monthly, according to Claude's pricing page.

Claude for Beginners: Chat, Projects and Claude Code

These three names sound alike, so here's the short version.

Chat is the empty box you see when you log in. Use it for one-off jobs: rewrite this email, summarise this document, help me plan this trip.

Projects are separate workspaces, each with its own chats, files and instructions. Anthropic's help page describes them as "self-contained workspaces with their own chat histories and knowledge bases." Think of one Project per client. You upload their brand guide or FAQ once, write a few lines about their tone, and every chat in that Project starts with it. Free accounts get up to five Projects.

Claude Code is the version that works on your computer instead of only in a chat window. Anthropic calls it "a coding tool," but it also runs multi-step jobs for you: creating files, running checks, one step after another. It's included with paid plans, not the free one.

What I Use for What

I spend most of my Claude time in Claude Code, and I'm not a developer. I use it with skills (more on those below) for topic research, video scripts, putting together videos and thumbnails, and planning blog posts like this one.

If you want to use Claude as a personal assistant for your clients, I'd map it like this. Quick drafts and replies go in chat. Anything you do again and again for the same client goes in that client's Project. Long, multi-step work, the kind with ten steps and a checklist, is where Claude Code starts to pay off.

You can also connect Claude to the apps you already use. Its Google Workspace connectors (Gmail, Google Calendar and Google Drive) are available to all users, according to Anthropic's help centre. There's a Microsoft 365 connector too, but it needs a work Microsoft account (a business Microsoft 365 setup). Personal Outlook.com addresses can't connect.

Skills: Teaching Claude Your Way of Working

A skill is a folder of written instructions that teaches the AI one specific task. Anthropic describes it as "a folder containing a SKILL.md file that teaches an agent a specific task or domain." An agent here means an AI that can take actions on its own, not just answer questions.

I have skills for research, for checking hooks and headlines, and for making my writing sound less like AI. I never re-explain those jobs.

On October 22, Anthropic said it was donating Agent Skills to the Agentic AI Foundation and that "more than 70 agent platforms, including ChatGPT, Cursor, GitHub Copilot, and VS Code" have added support. So a skill you write for Claude isn't locked to Claude.

Auto Mode Is Coming

Also on October 22, Claude's official account posted that "starting next week," sessions in the Claude web, desktop and mobile apps "will run in auto mode by default on Pro, Max, and Team plans."

In plain words, Claude will ask for your approval less often, and "an automated check blocks risky actions" instead. You can switch back to manual whenever you want. If you're working inside a client's connected accounts, I'd keep an eye on this setting once it arrives.

Client Data: The Privacy Basics

This is the part I'd never skip. On the Free, Pro and Max plans, there's a privacy setting for whether your chats can help train future models. You'll find it under Settings, then Privacy. If you turn it off, new chats won't be used for future training.

Claude also has incognito chats. Anthropic's privacy FAQ says they "are not used to improve Claude, even if you have enabled Model Improvement" in your settings.

On Anthropic's business products, including Team and Enterprise plans, the default is that your chats aren't used for training.

The bigger rule is yours to keep. Don't paste passwords, bank details or anything your client hasn't agreed to share with an AI tool. When in doubt, ask them first.

What This Means for VAs

Claude doesn't need to be complicated. Make one Project for your busiest client this week. Add their tone notes and one key document, then use it for every draft you write for them.

When the same job keeps coming back, turn it into your first skill.

Day in the Life of an AI-Powered VA: My Real Week

2026-11-02T19:32:00.000+08:00

This week, from Thursday to Saturday, I made two YouTube videos, two Shorts, and a 15-post blog plan, and Claude fact-checked 295 claims along the way. And I didn't film a single frame.

This is what a day in the life of an AI virtual assistant actually looks like for me. It's not a morning routine with a perfect latte. It's the real list of what I did, what Claude did, and what went wrong.

A Day in the Life of an AI Virtual Assistant: My Setup

Everything ran through Claude Code inside the Claude desktop app. Claude Code is the version of Claude that can use tools on your computer, like opening files, searching the web and running programs, instead of only chatting.

The videos are for my small test channel, The 1% Mindset. I pick the topics, approve every script, and say no when something looks off. Claude does the heavy lifting in between.

First: Research Before Anything Else

Both videos started with research, and this is the part I'd show any VA first.

For each topic, Claude sent several research helpers out at the same time, each looking at a different angle: the company's own pages, posts on X, and other YouTube videos. Then a separate set of checkers opened every source again and marked each claim as verified, partly right, unverified, or wrong.

The first topic was MiniMax M3.1 Flash, a new AI model. That ledger, which is just a list of every claim with its verdict, ended up with 194 claims. 155 checked out, 25 were partly right, 10 couldn't be confirmed, and 4 were wrong.

The second was Kimi K3.1, from the Chinese company Moonshot. That one had 101 claims: 76 verified, 19 partly right, 5 unconfirmed and 1 wrong.

That's 295 claims, and only the checked ones were allowed into a script.

Building the Videos

Once the facts were in, Claude wrote each script and I read and approved it. The voice-over comes from Kokoro, a free voice model that runs on my laptop. Most of the visuals are real screenshots of the sources, with red circles and underlines added by Claude.

The MiniMax video came out at 4 minutes 12 seconds. Its topic: a new AI model that guesses an answer 93% of the time when it doesn't know.

The Kimi video is 3 minutes 55 seconds, about what different AI models build when you ask them to build themselves in Minecraft.

Each long video took most of a day, with me reviewing at every step. On top of that came two Shorts, at 27.3 and 25.6 seconds. Claude also made the thumbnails with Nano Banana 2, Google's image model, and wrote the YouTube titles, descriptions and tags after checking what people actually type into YouTube search.

The Plot Twist: News Broke Mid-Project

Halfway through the Kimi project, Moonshot released Kimi K3.1 Pro (October 23, 17:15 UTC). Our script was suddenly out of date.

So we stopped and checked Moonshot's own launch post and its blog before changing a word. The script was rewritten the same day, using only what Moonshot itself had said. If you've ever had a client change the brief mid-project, you know that feeling.

The Part That Caught Real Mistakes

After each render, Claude pulled a frame from every 2 seconds of the video and checked them all, like a very patient editor.

It found a red circle sitting on the wrong bar of a chart, and an empty 8-second stretch where nothing was on screen. On the Kimi video, it spotted two screenshots that showed numbers we hadn't verified, and those were taken out.

None of these were big disasters. But each one is exactly the kind of thing a client would notice and I'd be embarrassed by.

Saturday: Planning This Blog

The last job was this blog. Claude ran the same research-then-verify process on what VAs search for: 61 claims checked (46 verified, 13 partly right, 2 wrong), 104 ideas, and a 15-post plan for this week.

The first post went live on Saturday: Can Claude Make YouTube Videos? I Made a 4-Minute One.

What This Means for VAs

Notice what I spent my time on. Not typing scripts or dragging clips around. I spent it deciding: which topic, which facts were good enough, which frame looked wrong, what to do when the news changed mid-project.

That's the job an AI-powered VA does. The tools handle volume, and you handle judgment. Clients can buy volume anywhere. Judgment is the part they keep you for.

If you want to start, copy just one habit from my week: never let a fact reach a client until something, or someone, has opened the source again.

Want the behind-the-scenes clips from weeks like this?

Claude Deep Research for VAs: How I Fact-Check Every Claim

2026-11-01T19:30:00.000+08:00

Claude's deep research feature can hand you a sourced report on almost any topic. Checking it is still your job.

The feature is officially just called "Research", and it's on every paid plan. Turn it on, ask one question, and Claude runs search after search, then writes you a report with its sources.

But a report isn't the same as facts you can trust. This week my research setup checked 356 claims for my videos and this blog, and the second check caught real mistakes. Here's how Claude Research works, and the fact-checking method I add on top.

What Claude Research Is (in Plain Words)

Normal web search in Claude looks something up once or twice. Research goes further. Anthropic's help page says Claude "runs multiple searches that build on each other and decides what to look into next based on what it finds."

Anthropic's guide on choosing between features says Research is best for questions that need five or more lookups over one to three minutes, like comparing a client's competitors, and that it pulls the results into a longer report with citations.

How to Use Claude Deep Research

Who gets it: Research is available on paid plans only (Pro, Max, Team and Enterprise), on the web, the desktop app and the phone app.

How to turn it on: web search has to be on first. Then click the "+" button at the bottom left of the chat and choose "Research". A blue marker shows it's on.

What it can read: the web, plus any connectors you've switched on for that chat. A connector is a link between Claude and another app, like Gmail, Google Calendar or Google Docs. On the phone app, Research only searches the web and quietly skips your connectors, so start important research on a computer.

The catch: it uses up your plan's limits faster than a normal chat, because it reads so many sources.

How I Research a Topic (My Real Method)

Here's where I'm honest about my own setup. This week I didn't use the Research button. I used Claude Code, the version of Claude that can run tools on my computer, and I built the research and checking into separate steps. But the method works with the Research button too.

Step 1: Several helpers, different angles. Claude sent out research helpers at the same time, each with one job: the company's own pages, posts on X, other YouTube videos.

Step 2: Separate checkers. A different set of checkers opened every source again, without trusting the first set's notes.

Step 3: A ledger. Every claim went into one list with a verdict: ✅ verified, 🟡 partly right (with the corrected wording), ❓ couldn't confirm, ❌ wrong.

Step 4: Only checked facts move on. Nothing reaches a script or a post unless it's ✅, or 🟡 with the corrected wording.

The numbers from this week: 194 claims for my MiniMax video, 101 for my Kimi video, and 61 for this blog's plan.

What the Fact-Check Actually Caught

Three examples, so you can see why the second step matters.

The first research pass wrote that freelancers' earnings from AI skills grew 109%. Upwork's own release says something different: skills that mention AI grew 109% in demand, not earnings.

A careers site said the US government expects admin assistant jobs to drop 10%. The US Bureau of Labor Statistics projection for 2024 to 2034 was actually 0%, "little or no change". Its newer projection, for 2025 to 2035, is a 2% decline.

And the first pass decided that Space Bunny, a mystery AI model later revealed as MiniMax M3.1 Flash, was "not #1" on OpenRouter, a site that ranks AI models by use. Half right. It was #2 over the month, but #1 on the daily chart for 10 straight days.

Two of those came from a first pass reading a real source too quickly. One came from a website misquoting a government page. Every one of them looked believable, and that's exactly what a client report can't afford.

Do This With the Research Button

You don't need my setup to copy the method. Run Research on your question. Then open a new chat and paste in the report with this prompt:

"For each factual claim in this report, open the source and quote its exact wording. Mark each claim ✅ verified, 🟡 partly right (give the corrected wording), ❓ couldn't confirm, or ❌ wrong."

Then only use the ✅ claims, and the 🟡 ones in their corrected form.

What This Means for VAs

Research is one of the most common things clients hand to a VA: a competitor list, a supplier check, a "find out what's new in our industry". Claude Research does the searching for you.

What it can't do alone is take responsibility for the answer. A ledger with ticks and crosses is something you can show a client. It says "I checked this", and that's worth more than any fast summary.

Try it on your next research task.

AI News This Week (Oct 25–31): What Changed for VAs

2026-11-01T08:57:56.293+08:00

AI news this week was mostly about your inbox, your calendar and your client's slide decks. Here are the updates from October 25 to 31 that change how a virtual assistant works, and one outage you should know about.

I've grouped them by the part of your day they touch, and every date comes from the company's own announcement.

Inbox and Calendar: ChatGPT Reaches Apple Users

On October 27, OpenAI added iCloud Mail and iCloud Calendar to ChatGPT. It can find emails, catch you up on your inbox, check your schedule and update events. OpenAI says it's available on all ChatGPT plans, on the web, the desktop app and iPhone.

This matters if you have a client who lives in Apple Mail instead of Gmail or Outlook.

Then on October 30, the ChatGPT phone app started letting you stay signed in to two accounts and switch between them. Each one keeps its own chats, memory and settings. If you use your own account plus a client's Business workspace, this keeps the two from mixing.

The Outage Nobody Talked About

Here's the story I found most useful, and it got almost no coverage. Claude's status page shows its Google Calendar connector (the link that lets Claude read your calendar) had errors from October 26 to October 28, about two and a third days. A separate Gmail connector problem lasted about six and a half hours on the evening of October 27, Pacific time.

Claude's status page showing the Google Calendar connector incident, October 26 to 28

So if Claude told you "there's nothing on the calendar Tuesday" during that window, check it again by hand. And if your whole inbox routine runs through an AI connector, keep a manual fallback for days like this.

Docs, Decks and Spreadsheets

On October 29, Google said the Gemini app can now preview and edit files right inside the chat: PDFs, Docs, Sheets, slide decks and plain text. Google says it's for all users wherever the Gemini app works. Making files in Gemini isn't new. Fixing them without copying everything into Docs first is.

Google Slides also got a bigger one. Gemini in Slides can now edit a whole deck from one request, like "make the titles on all slides the same font size and color." It's English only, on paid Workspace plans and Google AI Pro or Ultra, and Google says the rollout could take more than 15 days to reach you.

Two more for the design and data side. Canva launched Brush, a tool that edits an existing design from a prompt, for Canva Pro subscribers. And Microsoft's October Copilot roundup confirmed a "Clean data" skill in Excel that finds duplicates and messy formatting, then asks you to approve each fix. That one needs Microsoft 365 Copilot.

Team Chat and Meetings

Slack's Slackbot can now run a task on its own when something happens, like a new message or an emoji reaction. Think "every time a client posts in #requests, add it to my list." Slack says what you get depends on your plan and your admin, so ask before you count on it.

Notion said on X that its Meeting Notes extension for Google Meet now labels each speaker by name, instead of just "you" and "everyone else." That makes action items much easier to assign. Notion also says its Agent can now build Excel, Word, PowerPoint and PDF files for you.

New Models and Plan Changes

OpenAI released GPT-6.1 Terra on October 26. A model is the AI brain behind the chat, and OpenAI pitches this one for "computer use" (the AI clicking through apps for you) and "clear, natural writing." It's in ChatGPT for Plus, Pro, Business, Enterprise and Edu, and your usage limits stay the same.

On the Claude side, two quieter changes. On October 29, Haiku 4.5 left the model picker and anyone using it was moved to Haiku 5.5, so check any saved prompts you ran on it. And on October 30, Anthropic renamed its top plans: Max 5x is now Max ($100 a month) and Max 20x is now Ultra ($200). Verified Ultra accounts no longer have the five-hour limit. The weekly limit didn't change, despite what some posts say.

The Bigger Picture

Anthropic, the company behind Claude, made its IPO filing public on October 26, planning to list on Nasdaq as "ANT." You may have seen "$2 trillion IPO" headlines. The filing doesn't set a price or a valuation yet.

Closer to home, the Foundation for Media Alternatives published a Philippine AI risk report on October 30. Job loss and "labor intensification" (more work, faster, for the same pay) came out as the most widespread risks. The Philippines still has no AI law, and the report says seven House bills are being merged into one.

What This Means for VAs

Look at the pattern. Almost every update this week moves AI deeper into the tools we already manage for clients: mail, calendars, decks, Slack, meeting notes. That's good news for us, because someone still has to set these up, check the output and notice when a connector quietly breaks for two days.

That last part is the skill. The FMA report warns about "more work, same pay," and the way around it is to sell the judgment, not the hours. A VA who can say "I connected your calendar to AI, and I double-check it every morning" is offering something a tool can't.

My pick for this weekend: if you have an Apple-using client, test the ChatGPT iCloud connection on your own account first, then decide if it's safe for theirs.

Which of these updates are you trying first?

Arc Is Shutting Down Dec 22: Move Client Workspaces Now

2026-10-31T19:29:00.000+08:00

Arc is shutting down on December 22, 2026, and after that the browser stops working on Mac and Windows. If you keep each client in their own Arc Space, you have until then to move everything, and it takes a few clicks if you do it before the deadline.

Here's the Arc browser shutting down timeline, the easiest way to move, and a plain backup plan if you'd rather not use Dia.

Arc Browser Shutting Down: The Dates

The Browser Company, the team behind Arc, announced the shutdown on October 22. Their own farewell page at arc.net lays out three dates.

October 22: nobody can create a new Arc account. Existing accounts keep working, and the latest version of Arc adds an option to export your data.

November 23: on iPhone and Android, Arc Search loses "Browse for Me" and its other AI features. It still works as a basic browser, but you can't sign in or sync tabs anymore.

December 22: Arc stops working on Mac and Windows, and Arc Search for Android is removed from the Google Play Store and from phones. Accounts are deactivated, and Arc says personal data is deleted within 30 days. From that date, Arc says it can no longer retrieve your data for you.

The Easy Move: Arc to Dia

Dia is the newer browser from the same team. It's free, and its AI features are optional. Arc's page says you don't need to export anything first if you're moving to Dia.

If you don't have Dia yet: download it from diabrowser.com, and when it asks which browser to import from, pick Arc. Arc says it brings over your passwords, history, tabs, Profiles, Folders, bookmarks and extensions, on both Mac and Windows.

If you already have Dia: on a Mac, go to Dia > Import from Another Browser in the menu bar. On Windows, open the Dia app menu and choose "Import from another browser."

A few names change on the way. Your Spaces become Profiles, and you can still swipe between them. Folders become Tab Groups. Pinned tabs and Favorites come over with their custom names. If you miss the vertical tabs, choose "Sidebar tabs" in Dia.

One setting to check before you open client work in Dia. Dia's FAQ says that unless you opt out in the Privacy Panel of Dia's settings, your content data is shared to improve Dia (not tied to your profile, and deleted after 30 days). If a client's contract is strict about data, turn that off first.

The Backup Plan: Save Your Tabs as Links

Maybe your client doesn't want a new browser, or you'd rather go back to Chrome. Arc lets you save your Spaces and tabs as a list of links.

On a Mac: open Arc > Settings, go to Advanced, then choose Export Data and save the file. On Windows: press Ctrl+comma to open Settings, find "Export Spaces and tabs" in the Advanced section, and choose Export Data.

Arc's page only covers exporting tabs this way. It doesn't give steps for moving saved passwords into another browser. So before you leave Arc, check that every client login also lives in a password manager, not only in the browser.

If you do land in Chrome, its help pages say Chrome imports bookmarks from an HTML file, and passwords only from a .csv file. Google also warns you to delete that .csv afterward, because anyone using the computer can open it.

What This Means for VAs

If you use Arc for client work, your Spaces might be the whole system: one Space per client, their tools pinned, their logins saved. Losing that in a rush would mean a messy week of password resets and "can you resend that link?"

Here's how I'd handle it. Pick a quiet afternoon this week. Import into Dia, then open each client's Profile and click through their pinned tabs to check you're still logged in. Write down anything that didn't come across. Then export your tabs from Arc anyway, as a backup file.

If you set up Arc for a client, send them a short heads-up with the December 22 date. That two-minute email can save them a lot of panic in December.

Moving browsers this week? Tell me which one you picked.

How to Become an AI Virtual Assistant: Skills That Pay

2026-10-30T19:27:00.000+08:00

Here's how to become an AI virtual assistant: learn to do real client tasks with AI, then prove you can check what it gives you. You don't need to code. You need a handful of skills, and the 2026 data says clients are already paying more for them.

I use these skills every day as the VA for an AI YouTuber. Below is the roadmap I'd follow if I were starting today.

Why AI Skills Pay Right Now

Upwork's Future Workforce Index 2026 found that freelancers doing AI work on its marketplace earn 34% more per hour than those not using AI in their work.

In its Q2 2026 results, Upwork said spending on AI-related work grew more than 22% year over year, even while total spending on the platform fell 4%.

The Future Workforce Index even has a section called "AI orchestrator." That's a fancy name for a person who directs AI tools to get a job done. A good VA already directs people, calendars and inboxes. This is the same muscle.

The AI Skills for Virtual Assistants I Use Daily

1. Research with AI, then fact-check it. AI is fast at finding sources. It's also confident when it's wrong. My rule: every number and date goes back to the original page before it goes into my client's work. That checking step is the part clients can't skip, and it's the part they pay for.

2. Drafting and scripting with AI. AI writes my first drafts of video scripts. I read every line and sign it off before anything else happens, the same job I'd do for a human writer. Knowing what your client would never say is a skill AI doesn't have.

3. Building small things with Claude Code. Claude Code is a version of Claude that can run tools on your computer, not just chat. I've used it to research, write and render a whole YouTube video. You don't start there. Start by asking it to rename and sort a messy folder.

4. Picking the right tool for the job. Some tasks need a chatbot, some need an automation, and some need a human. Knowing the difference saves your client money.

AI Automation Virtual Assistant Jobs

Automation means connecting apps so a task runs by itself. Say a new form entry automatically lands in a spreadsheet and sends a welcome email. That's the work behind most "AI automation virtual assistant jobs."

Demand is real. In Upwork's In-Demand Skills 2026 report, AI integration (connecting AI to the tools a business already uses) grew 178% year over year, and AI video generation and editing grew 329%.

VAs are hungry to learn this too. A Filipino YouTube video, Kuys RJ's "10 AI Automation Projects That WILL Get You Clients," got about 31,000 views, around 5.4 times what his videos usually get.

My advice: build one small automation for yourself first. Then show it to a client as a before-and-after.

Pick a Growing Niche

Upwork's same report lists the fastest-growing skills in customer service and admin support: Ecommerce Management (+130%), Medical Virtual Assistance (+44%) and Supply Chain and Logistics Project Management (+37%). General virtual assistance is still the most in-demand skill in that category.

So you don't need to stop being a VA. Add AI to a niche that's already growing.

Make Your Profile Easy for AI to Read

On August 10, Upwork launched an MCP server. MCP is a standard that lets AI tools like Claude and ChatGPT connect to other services. With it, a client's AI agent (an AI that can take actions, not just answer questions) can post a job, surface talent and summarize proposals as they arrive.

That means an AI might read your proposal before your client does. Write it plainly. Name the tools you use and the results you got, in the first lines.

Freelancers can use it too. Upwork says your own agent can flag best-fit jobs and draft a tailored proposal. Just read every draft before you send it.

How to Become an AI Virtual Assistant in 3 Months

Here's the roadmap, one month at a time.

Month one: learn one AI assistant well, Claude or ChatGPT, and use it on your real tasks. Month two: practice research and fact-checking until checking is a habit. Month three: build one automation and one small project you can show.

What This Means for VAs

Skilled freelancing is growing. Upwork says it rose from 28% of US skilled knowledge workers in 2025 to 38% in 2026. More people are competing, and AI skills are how you stand out.

Keep a simple portfolio of before-and-afters as you go. Clients don't hire "knows AI." They hire "saved me hours every week."

"We Bought You ChatGPT": Setting AI Expectations With Clients

2026-10-29T19:26:00.000+08:00

When a client thinks AI makes everything instant, you fix it with one short conversation about scope, time, checking and price, before the work starts. Setting realistic AI expectations with clients isn't about saying no to AI. It's about showing them the part of the work they can't see.

Here's a real example of how it goes wrong, and the exact words I'd use to stop it.

The 500-Images-a-Week Story

In September, someone posted on Reddit's r/buhaydigital with the title "Local company expects us to generate 500 AI images PER WEEK."

They work in-house in marketing at a mid-sized online retail company. After the company bought AI tools, 500 AI images a week, about 2,000 a month, got added to their KPIs (the targets their performance is measured on). That was on top of their existing workload.

The poster summed up management's attitude like this: "We bought you Chatgpt, so just generate a bunch of photos."

Then they listed what one usable image really takes: writing prompts, generating several versions, checking for AI errors, making sure the products look accurate, fixing things and regenerating. One reply (11 upvotes) said it best: "Sounds like management is hallucinating worse than AI."

The top reply (17 upvotes) suggested a smarter move: run a small pilot batch first and measure how long the checking actually takes.

Why AI Expectations With Clients Go Wrong

Clients see the fast part. They type a prompt, get an answer in seconds, and think the job is done.

They don't see the checking. AI "hallucinates," which means it states wrong things with total confidence. It can also put the wrong label on a product or invent a statistic. Someone has to catch that, and that someone is you.

There's a second problem. On r/freelance, in a thread called "AI has completely changed freelance work and honestly, I'm struggling with it" (265 upvotes), one commenter (29 upvotes) put it this way: "a conversation can solve someone's problem now and they won't pay you for a conversation."

And a third: another r/freelance post (113 upvotes) was about a former client who came back eight months after the project ended, asking the freelancer to "fix" what ChatGPT says about them, for free.

Scripts for Talking to Clients About AI

You don't need a big speech. You need a few calm sentences ready before the topic comes up.

Scope. Agree on what "done" means, not just what AI can produce.

"AI can make dozens of drafts in an hour. What you're paying me for is the ones that are accurate and ready to post. Let's agree on how many finished pieces you need each week."

Time. Use the pilot idea from that Reddit thread.

"Before we commit to a number, let me run a small test batch this week. I'll time the whole process, including checking and fixing, and we'll set the target from real numbers."

Quality checks. Make the checking visible so it stops being invisible work.

"Every AI draft goes through my check before it reaches you: facts, names, numbers, and whether it sounds like your brand. That step is why you can post it without worrying."

Pricing. If AI makes you faster, don't hand all of that speed back as a discount. Price the result, and list the review as its own line.

"I use AI to speed up the first draft, so you get more output for the same budget. My rate covers the finished, checked work, not the minutes the tool takes."

Old work. The eight-months-later thread had a fix in the comments (21 upvotes), a line you can put in your contract: "the deliverable reflects the facts at the time it's delivered. anything that changes after that is a new engagement, new quote, no exceptions."

What This Means for VAs

When a client says "just use ChatGPT," they're telling you they don't see the work AI doesn't do. That's an opening.

The VA who explains the checking, runs a quick test and prices the finished result looks like a pro. The VA who quietly says yes to 500 images a week burns out.

Pick one of the scripts above and save it in your notes today. The next time a client gets excited about a new AI tool, you'll have your words ready.

If this helped, tell me the wildest AI request a client has sent you.

30 Free Virtual Assistant Prompts for ChatGPT and Claude

2026-10-28T19:22:00.000+08:00

Here are 30 free virtual assistant prompts you can paste into ChatGPT or Claude today, sorted by the task in front of you. Every one of them tells the AI to ask before it guesses.

I use prompts like these every day in my own VA work. Search for a list like this and almost everything that comes up is a paid prompt pack. So here's mine, free, and you can copy any of them straight off the page.

How to Use These Virtual Assistant Prompts

Anything in [square brackets] is yours to fill in: the client's name, their tone, the email you're working on. The more real detail you give, the better the answer.

You'll also see the same three habits in almost every prompt: ask me questions, flag what you're unsure about, and don't make things up. AI tools sometimes fill gaps with confident guesses. Those lines make it show you the gap instead, so you can check it before your client ever sees it.

One privacy rule before you paste anything. Leave out passwords, bank details and anything your client hasn't agreed to share with an AI tool. If you use Claude, its privacy settings have a toggle for whether your chats help train future models, and you can switch it off.

Inbox and Email

Email is where most of us start, so these come first. They sort, draft and nudge, and none of them send anything. You stay the one who hits send.

1. Sort this inbox

You're helping me manage [client name]'s inbox. Below are [number] emails. Sort them into: Reply today, Reply this week, FYI only, Can archive. Give one line on why for each. If you can't tell how urgent something is, put it under "Not sure" and tell me what you'd need to know. Don't guess at deadlines that aren't written in the email.
[paste emails]

2. Reply in my client's voice

Draft a reply to the email below for [client name], who is [role] at [company]. Their tone is [warm and brief / formal / casual]. The reply needs to [goal]. Keep it under [100] words. Don't promise dates, prices or anything that isn't in the email or my notes. If something is missing, write [CHECK: what's missing] in the draft instead of filling it in.
Email: [paste]
My notes: [paste]

3. Polite follow-up

Write a follow-up email to [recipient] about [topic]. We first wrote on [date] and haven't heard back. Give me two versions, one friendly and one firmer, both under 80 words. If you need the original email to get the details right, ask me for it first.

Calendar and Scheduling

Time zones are where small mistakes get expensive. These prompts make the AI show its working, so you can spot a wrong offset before a client misses a call.

4. Find a time across time zones

[Client name] is in [time zone] and works [hours]. The other person is in [time zone] and works [hours]. Suggest 3 meeting slots in the next [5 working days] that fit both working days. Show each slot in both time zones. If daylight saving time could change the offset on any of those dates, say so and tell me to double-check. Don't assume.

5. Review next week's calendar

Here is [client name]'s calendar for next week: [paste]. Point out double bookings, back-to-back meetings with no break, days with no time for focused work, and events missing a location or video link. Don't move anything. Just list what you'd flag and a suggested fix for each.

6. Offer meeting times by email

Write a short email to [person] offering these times for a [30-minute] call with [client name]: [times, with time zone]. Ask them to pick one or suggest another. Their time zone is [time zone]. If any slot falls on a weekend or very early or late for them, point it out to me before you write the email.

Meeting Notes

A transcript is only useful once someone pulls the decisions out of it. These three get you from messy notes to something your client will actually read.

7. Transcript to action items

Below is the transcript of [meeting name] on [date]. Pull out: decisions made, action items (with owner and due date) and open questions. Use only what's in the transcript. If an action item has no clear owner or date, write "owner unclear" or "no date given" instead of guessing.
[paste transcript]

8. Recap email

Turn these meeting notes into a recap email from [client name] to [attendees]. Start with the 3 most important decisions, then the action items with owners. Keep it under [200] words and easy to skim. Copy numbers and dates exactly as they appear, and mark anything that looks contradictory with [CHECK].
Notes: [paste]

9. Brief before a meeting

[Client name] is meeting [person or company] on [date] about [topic]. Using only what I paste below (past emails, their bio, old notes), write a one-page brief: who they are, what we've discussed before, open items, and 3 questions [client name] could ask. List anything important that's missing under "Gaps" at the end.
[paste]

Research

Research is where an AI is most tempted to sound sure when it isn't. Each of these asks it to separate what it knows from what you should check.

10. Compare options for a client

I'm researching [type of tool or service] for [client name], a [type of business] with a budget of [budget]. Compare [option A], [option B] and [option C] on price, main features and [what matters most to the client]. For every price or feature, tell me how sure you are. If you can't confirm current pricing, say so plainly so I can check the official site.

11. Quick research brief

Write a short research brief on [topic] for [client name], who needs it to [purpose]. Start with a 3-sentence summary, then the key points, then what's still uncertain or disputed. Keep facts you're confident about separate from things I should verify. Don't make up statistics or sources. If my request is unclear, ask me up to 3 questions before you start.

12. Find the claims I need to check

Here's a draft [post / email / report] for [client name]: [paste]. List every factual claim in it: numbers, dates, names and quotes. For each one, tell me whether it needs a source and what kind of source would confirm it. Don't mark anything as true unless it's common knowledge.

Client Communication

These are for the messages that need a bit of care: a tone fix, an awkward conversation, or the weekly update your client skims on their phone.

13. Fix the tone, keep the facts

Rewrite this message to [name] so it sounds [calmer / warmer / more professional]. Don't change any facts, dates or amounts. Under the rewrite, list what you changed and why in a few short lines.
[paste message]

14. A tricky message

Help me write a message to [client name] about [situation, e.g. a delay or a request outside our agreement]. What I want: [outcome]. What I can offer: [options]. Keep it honest and short, with no over-apologising. Ask me questions first if you need more context about our agreement.

15. Weekly update

Write a weekly update for [client name] from me, their VA. Done this week: [list]. In progress: [list]. What I need from them: [list]. Put the "need from you" part first and keep it under [150] words. Don't add tasks I didn't list.

Social Media

Good for first drafts of client posts. The rule in all three: no claims, prices or stats the client didn't give you.

16. Caption options

Write 3 caption options for [platform] for [client or brand]. Their audience is [audience] and their tone is [tone]. The post is about [topic or photo description]. End with this call to action: [CTA]. Keep each under [number] characters. Don't add claims, prices or stats I didn't give you.

17. Two-week content plan

Plan [2 weeks] of [platform] posts for [client name] using these themes: [themes] and these key dates: [launches, holidays]. Give me a table with date, post idea, format and an opening line. If a date might clash with something, like a public holiday in [country], flag it for me to check.

18. Repurpose one piece of content

Turn this [blog post / video transcript] from [client name] into [3] short [platform] posts. Use only points from the original and keep the client's own wording where you can. Flag any line you shortened in a way that might change the meaning.
[paste]

Documents and SOPs

An SOP is a standard operating procedure: written steps anyone could follow to do a task the same way. These turn your notes into documents without inventing missing steps.

19. Rough notes to SOP

Turn my rough notes below into a step-by-step SOP for [task] at [client's business]. Number the steps and add who does each one and which tool they use. Where my notes skip a step or aren't clear, add a [QUESTION: ...] line instead of filling the gap.
[paste notes]

20. Reusable template

Create a reusable template for [document type, e.g. onboarding checklist / meeting agenda / invoice reminder] for [client name]. Use [square brackets] for every detail that changes each time. Before you start, ask me which sections they usually include.

21. Proofread without rewriting

Proofread this [document] for spelling, grammar and consistency (names, dates, formatting). Don't change the style. Give me a list of each change with the original and the fix. If something looks factually wrong, flag it instead of changing it.
[paste]

Data Cleanup

Spreadsheet work is where a quiet AI guess can do the most damage. These keep blanks blank and leave the numbers alone.

22. Clean up a list

Below is a list of [contacts / products] from a spreadsheet. Fix capitalisation, split full names into first and last name, format phone numbers as [format] and remove exact duplicates. Don't invent missing data: leave blanks blank. At the end, list any rows you weren't sure about.
[paste]

23. Explain a formula

I'm working in [Google Sheets / Excel]. Column [A] has [what], column [B] has [what]. I want to [goal]. Give me the formula, explain in plain words what it does, and tell me what could break it, like empty cells or mixed date formats. Ask me for a sample row if you need one.

24. Spot problems, change nothing

Here is [client name]'s [expense list / invoice tracker]: [paste]. Point out possible problems: duplicates, missing dates, amounts that look unusual next to the rest, and totals that don't add up. Give the row for each one. Don't change any numbers.

Travel

Prices, visas and entry rules change, so these prompts make the AI organise and point you to official sources instead of quoting rules from memory.

25. Build the itinerary

[Client name] is travelling to [city] from [date] to [date] for [purpose]. Using the bookings I paste below, build a day-by-day itinerary with local times, confirmation numbers, addresses and travel time between stops. Flag any tight connection or anything missing, like a night with no hotel.
[paste bookings]

26. Shortlist options

Suggest [3] [hotels / areas to stay / flight routes] in [city] for [client name], who prefers [preferences] and has a budget of about [budget] per [night / trip]. Tell me what to search for and what to check. Don't state prices or availability as fact, because those change. I'll confirm on the booking site.

27. Pre-trip checklist

Make a pre-trip checklist for [client name]'s trip to [country] on [dates]: documents, apps, chargers, business etiquette and things to confirm 48 hours before. For anything about visas or entry rules, write "check the official government site" instead of giving rules from memory.

Managing Yourself

The last three are for you. Juggling several clients is a job in itself, and these help with the planning around the work.

28. Plan my day

Here is my task list for today across [number] clients: [paste]. I have [hours] hours and these fixed calls: [times]. Put the tasks in order with time blocks, deadlines first. If something won't fit, tell me what you'd move and ask me which client is safest to push.

29. End-of-day log

Turn these quick notes into a tidy end-of-day log for [client name]: what I finished, what's in progress, what's blocking me, and what's first tomorrow. Use only what I wrote.
[paste notes]

30. A personal assistant prompt for Claude

You're my assistant for [client name], a [role] at [company]. They prefer [tone] and [preferences, e.g. short emails, no meetings before 10 am, time zone]. When I give you a task: ask up to 3 questions if anything is unclear, never invent facts, dates, prices or names, mark anything you're unsure about with [CHECK], and never say something has been sent or booked. I do that part.

That last one works best as standing instructions. In Claude, you can save it as the instructions for a Project, which is a separate workspace with its own chats and files, so every chat inside it starts with your client's details already loaded. Free accounts can create up to five Projects.

What This Means for VAs

Prompts don't replace your judgment. They give it a head start. A good prompt gets you a draft in under a minute, and the [CHECK] marks show you exactly where your own knowledge of the client has to come in.

If you have a client who sends you a pile of meeting transcripts every week, start with prompt 7 and prompt 8 together. If inbox work eats your mornings, start with 1 and 2.

Then change the wording until it sounds like you and your client. The ones you end up using daily won't look exactly like mine, and that's how it should be.

Try one today, then tell me which prompt you started with.

Will AI Replace Virtual Assistants? What the 2026 Data Shows

2026-10-27T19:21:00.000+08:00

Will AI replace virtual assistants? From everything I've read this year, no, but it is replacing the easy parts of the job. The 2026 numbers show simple, repeatable tasks getting cheaper while AI-heavy work pays more, and that second group is where a VA can stand.

I'm a VA for an AI YouTuber, so I work with these tools every day. Here's what other VAs are saying, what the data shows, and what I'd do about it.

The Post 3,549 People Upvoted

In September, a post on Reddit's r/buhaydigital, a big Filipino remote-work community, was titled "My boss thought AI could replace my role." It got 3,549 upvotes and 149 comments.

The original story has since been deleted, but the replies are still there. One of the top replies, with 400 upvotes, came from someone who had been through it:

"Happened to me too. I resigned because of the so-called operations manager, then the CEO hired me back after a month. This time, I asked for a retainer with a minimum commitment, so they just renew it."

Another reply (129 upvotes) put the human edge in one line: "until AI can pay taxes, can be jailed, or can be blamed, professionally, it cant replace people."

That's accountability. Somebody still has to own the result, and a chatbot can't.

Not Every Story Ends Well

I won't sugarcoat this part. In October, a post on r/phcareers titled "im an advocate of AI until it caused me to be layed out" got 624 upvotes. The top reply (349 upvotes) said: "you probably got laid off for the company to hire a cheaper resource."

Look at what that reply is about: cost. In a lot of these stories, AI is the excuse and the budget is the reason.

Another r/phcareers thread, "AI ruined my work ethic, I might get fired eventually." (608 upvotes), was about the opposite problem: leaning on AI so hard that your own thinking gets rusty. The top reply (446 upvotes) was blunt: "Practice thinking and writing. For the next 2-3 YEARS. Yes, years."

A reply with 127 upvotes shared a habit I like: "I ensure I create the first draft and then use AI to refine wording, sentence structure, or formatting."

Will AI Replace Virtual Assistants? The Numbers

Reddit tells you how people feel. Here's what the bigger numbers say.

Upwork, one of the biggest freelance marketplaces, reported in August that the total amount clients spent on its platform fell 4% year over year in Q2 2026. In the same report, spending on AI-related work grew more than 22%.

Its CEO, Hayden Brown, said that "lower-complexity work continues to shift toward automation," while demand grows for "high-value AI talent, more complex projects, and new categories of work."

Upwork's Future Workforce Index 2026 adds detail. Freelancers doing AI work on its marketplace earn 34% more per hour than those not using AI in their work. And freelancers doing more complex work with AI saw their earnings rise 45% in Q1 2026.

There's a catch, though. In one category, "Generative AI and creative production," new contracts grew 90% while earnings per contract fell 13%. When anyone can do a task with one prompt, the price of that task drops.

What about official job numbers? The US Bureau of Labor Statistics projected "little or no change" (0%) in jobs for secretaries and administrative assistants from 2024 to 2034. Its newest update, for 2025 to 2035, shows a 2% decline, with about 314,400 openings a year.

That covers office admin jobs in the US, not remote VAs, but it's the closest official number we have. It's a slow shift, not a wipeout.

Why So Many Answers Feel Off

If you Google this question right now, one of the top results was written in 2016. That's six years before ChatGPT came out.

A lot of the fear you read, and a lot of the hype, is old or written by people who don't do this work.

What This Means for VAs

My honest view: AI is changing the work. It isn't ending it.

Tasks you can describe in one sentence, like "write a caption" or "summarize this email," are getting cheaper. The work that pays is everything around them: picking the right tool, catching what the AI got wrong, and owning the result for your client.

That's my day. AI helps me research and draft, and I check the facts and catch mistakes before anything goes out.

If you're worried, try three things this month. Learn one AI tool properly instead of ten badly. Write your own first draft before you ask AI to polish it, so your thinking stays sharp. And if a client starts talking about replacing you, remember that 400-upvote reply: offer a retainer with a minimum commitment, so you're something they renew, not something they cancel.

Want to see what AI-assisted VA work looks like up close? Read how I made a full YouTube video with Claude.

Gmail AI Inbox: The Catch for VAs Managing Client Inboxes

2026-10-26T19:19:00.000+08:00

Gmail AI Inbox is rolling out to paid Google Workspace accounts, and it turns a busy inbox into a to-do list. The catch for VAs: Google's help page says it doesn't support delegated inboxes, which is how a lot of us get into a client's email.

So here's what it does, who actually gets it, and how to make it useful for your client anyway.

What Gmail AI Inbox Does

Google announced the Workspace rollout on October 15, 2026. AI Inbox is a separate view in Gmail with two sections.

"Suggested to-dos" pulls action items, deliverables and deadlines out of email threads. Think "send the signed contract by Friday," lifted out of a long reply chain. "Topics to catch up on" gives short summaries of your most important threads, so you can see the latest decisions without reading 14 messages.

It's part of Gemini, Google's AI, so it's built into Gmail itself. Your client doesn't install anything.

Who Gets It (Read This Before You Promise Anything)

This is where I'd slow down before telling a client "you have this now."

It's only for users in the US with English (US) as their language. Google's list names Business and Enterprise (Starter, Standard and Plus), Frontline Plus and AI Pro for Education, but the same post says Starter and Education editions get it later, "in the coming months." So for now, count on Standard and Plus. Personal Google accounts need a Google AI Plus, Pro or Ultra plan.

There's no admin switch for it. It rolls out over up to 15 days: Rapid Release domains started October 12, and Scheduled Release domains start October 26. If your client's account is on Scheduled Release, it may show up any day in the next two weeks.

The Fine Print for VAs

Google's help page lists a few limits that matter if you manage someone else's email.

It reads only the Primary tab, not Social or Promotions. It skips anything archived, deleted, muted or snoozed. And it "does not support attachments, delegated inboxes, or encrypted emails." Delegated access is when your client lets you open their Gmail from your own account. If that's your setup, AI Inbox won't help you there.

It also needs Gmail's smart features turned on, and Google says your first to-dos can take up to 7 days to appear.

One more: the "Mark all as read" button in AI Inbox also marks those emails as read in the regular inbox. If your client relies on bold unread emails as their reminder system, don't click it on their behalf.

Gmail Live: The Voice Version

Google rolled out a second feature the same day, Gmail Live. On the Gmail phone app, you tap Live in the search bar and ask your inbox a question out loud, like "when is my next mandatory training due?" It answers by voice and shows a card with the source email every time, so you can check where the answer came from.

Same rule: US users with English (US) only. It's on by default for organizations with Gemini, across Business Starter, Standard and Plus, Enterprise plans, and Google's consumer AI Plus, Pro and Ultra plans.

What This Means for VAs

If your client is in the US on an eligible plan, their inbox is about to start writing its own to-do list. That's good news for you, not bad news.

Here's how I'd use it. Ask your client to open AI Inbox once a day and forward you anything in "Suggested to-dos" they want handled. You turn their list into action: the reply, the calendar block, the follow-up. Their AI spots the task, and you get it done.

If you work through delegated access, keep your own triage routine. AI Inbox won't see what you see, so your morning sort is still the one your client can trust.

And if your client is outside the US, or on Business Starter, you have time. Learn how it works now so you're the one explaining it when it arrives.

Want more updates like this one, translated for VAs?

Claude Duet Is a Robot Assistant: Where VAs Still Win

2026-10-25T20:00:00.000+08:00

Claude Duet is Anthropic's new app that works like a robot assistant: it briefs you every morning, keeps an eye on your inbox and calendar, and asks before it sends anything. If you're a VA, that reads a lot like your job description. It doesn't replace you, and below I'll show you exactly where you still win.

Quick note first: I haven't used Claude Duet. Almost nobody has yet, because it's a research preview with a waitlist. So this is an explainer built from Anthropic's own pages, not a review.

What Claude Duet Actually Is

Anthropic opened Claude Duet on October 21, 2026, as a research preview. That means an early version they're still testing with real users.

It's a separate app for iPhone and Android, and you can also open it on claude.ai. Anthropic calls it "a proactive agent." An agent is an AI that can take actions on its own, not just answer questions. Proactive means it comes to you first instead of waiting for you to type.

Who can get it right now: Claude Pro and Max subscribers in the US, through a waitlist. The FAQ says Team and Enterprise plans are "not included yet."

What It Does All Day

Each morning it sends a short brief on the most important things across your connected apps. After that, it checks those apps every few hours and messages you only when something needs you. Anthropic says it doesn't check nonstop because that would use up your plan's limits much faster.

You can also hand it tasks by text or voice, all in one ongoing thread. The announcement gives examples like finding Gmail and Slack messages you missed, checking QuickBooks or Stripe for unpaid invoices and drafting reminders, preparing payroll in Gusto, and drafting follow-ups from HubSpot. It works on its own computer in the cloud, so your laptop doesn't have to be on.

One early user, Lydia Hallie, whose X bio says she works on Claude Code at Anthropic, posted that she has it "watch Slack/email/cal and only ping me when it matters." She works at the company that makes it, so read that as an insider's setup. The post still got 1,049 likes.

The Rules It Can't Break

This is the part I'd read twice if I were a client.

Before Claude Duet sends a message, makes a payment, signs a document, or deletes anything, it shows you exactly what it's about to do and waits for your OK. Some things are off-limits even if you say yes, like trading securities or moving money between accounts.

Anthropic's safety post, published two days later, adds that it never sees your passwords. When a site asks for one, you type it into a form inside the app, and it goes to Duet's cloud browser without passing through the AI.

Where a VA Still Wins

Anthropic is upfront about the limits. Its own FAQ says Duet "may misread a thread or miscalibrate what's urgent," and it can miss something that arrives between check-ins. The announcement also says personal errands, like booking tickets, "aren't quite as strong" yet.

That's your gap. Knowing which email is actually urgent comes from months of context: which client panics, which "quick question" is really a fire, which Tuesday is already packed. A check every few hours doesn't have that.

And because Duet stops and asks before it sends, pays, signs or deletes, someone has to look at each request and say yes or no. For a busy founder, that someone could be you.

What This Means for VAs

Anthropic says it built Claude Duet for "professionals and small business owners with more on their plates than they can track." That's exactly who most of us work for. So expect a client to ask, "Do I still need you if I have this?"

Here's how I'd answer. A tool like this flags and drafts. You decide, follow up, and fix what it misreads. If your client gets access, offer to set it up with them: connect only the apps it needs, tell it what counts as urgent for this business, and review its morning brief together for the first two weeks.

That's a new service on your menu, not a lost job. If you're in the US on Pro or Max, you can join the waitlist on the Claude Duet product page and learn it before your clients do.

If a client has already asked you about tools like this, tell me what they said on the comments below.

1Password for Agents: AI Logs In, Never Sees Client Passwords

2026-10-24T19:24:21.031+08:00

1Password for Agents lets an AI agent log in to a website for you without ever seeing the password. The agent asks, you approve in 1Password, and 1Password types the login straight into the site.

If you've ever been tempted to paste a client's password into an AI chat so it can "just finish the task," this is the safer way. Here's how it works, who can use it today, and what I'd tell a client.

The Problem It Solves

An agent is an AI that does tasks on its own, like filling in a form or checking an order status, instead of only answering questions. Agents are getting good at this. But the moment one hits a login page, it stops.

1Password's announcement on October 22, 2026, describes the two bad options you have today. You take over and type the password in yourself every time, which is slow. Or you paste the password into the prompt, which gives the agent your login "potentially beyond the task at hand."

The post also points out that agents can't use passwords saved in your browser or written in notes. So without something like this, you keep getting pulled back in.

How 1Password for Agents Works

When the agent reaches a login, it asks for the credential it needs. You get the request in 1Password, check which login it wants, and approve it. Then 1Password fills in the username and password directly on the site. The agent never gets the password or the one-time code.

In 1Password's own example with Muse, you pick which login the agent can use, and it doesn't get anything else in your vault. You can also disconnect the integration at any time.

There's a nice side effect for anyone who changes passwords often. Your logins stay in one vault, so when a password changes, you update it once and every connected agent uses the new one.

Which AI Agents Work With It

Not all at once, and this is where I'd be careful with clients.

Two integrations are "rolling out over the coming days": Muse from Meta, and Grok Bot from SpaceXSI. Anthropic's Claude Duet, OpenAI's ChatGPT Work and dots, and Townie from Town are "coming soon." A longer list, including Perplexity, Browserbase, Browser Use and Ramp, is also marked "coming soon."

1Password adds that "the safeguards and controls vary by integration." So check each agent's own setup before you rely on it.

Is It Free?

For many people, yes. 1Password says it's available "at no additional cost" for Individual and Family accounts on 1Password.com.

Two limits. Accounts on 1Password.eu or 1Password.ca aren't currently supported. And the post only mentions Individual and Family accounts, so don't promise it to a client whose team uses a 1Password Business account.

What This Means for VAs

More clients are going to want an AI agent set up for them. Logins are the scary part of that job, and now there's a clear rule you can offer: the agent asks, a human approves, and nobody pastes a password anywhere.

Here's how I'd put it in practice. Write one line into your client SOP (standard operating procedure, your written how-we-do-things doc): "AI agents get logins through 1Password approval only, never a pasted password." Then list which agent the client uses and whether it's live yet or still "coming soon."

If your client is waiting on Claude Duet or ChatGPT Work, you have time to tidy their vault first. Remove old logins and name items clearly, so the right one is easy to spot when an approval request comes in.

You become the person who makes the AI safe to use. That's a skill clients will pay for.

If you're setting up agents for clients, tell me which one they picked.

Can Claude Make YouTube Videos? I Made a 4-Minute One

2026-10-24T18:33:15.478+08:00

Yes, it can. This week I made a full 4-minute YouTube video with Claude: the research, the script, the voice, every scene, and the thumbnail. But it didn't happen the way most people imagine, and the part that surprised me most wasn't the video at all.

Here's exactly what Claude did, what I did, and what I'd tell any VA who wants to try it.

First, What "Making a Video" Meant Here

Claude didn't press a magic "make video" button. There's no camera and no AI-generated footage in this video.

I used Claude Code, the version of Claude that can run tools on your computer, not just chat. It wrote the video as a set of animated web pages, then recorded them frame by frame into an MP4. Think of it like building slides that move, then filming them.

The video is for my small test channel, The 1% Mindset. The topic: a new AI model that guesses an answer 93% of the time when it doesn't know.

How It Went, Step by Step

Research. I asked Claude to dig into the topic. It sent several research helpers out at once, then a separate set of fact-checkers who opened every source again. They checked 194 claims. Four were wrong, and none of those made it into the script.

Script. Claude wrote a 4-minute script in plain English, then checked its own opening against a retention checklist and rewrote the first lines. I read it and signed it off before anything else happened. That's the same job I'd do for a human writer.

Voice. The voice-over comes from Kokoro, a free voice model that runs on my laptop. Claude split the script into sections, voiced each one, then transcribed the audio back to check that every word came out right.

Visuals. Every chart and post you see is a real screenshot of the source. Claude added the red hand-drawn circles and underlines, timed to the voice. The only made-up art is the cartoon test sheets and bunny from the thumbnail, plus a few simple bar graphics.

Thumbnail. Claude wrote the prompt for Nano Banana 2 (Google's image model), then added the "100% SURE" text on top.

The final file: 4 minutes 12 seconds, full HD.

What Surprised Me

The checking. After the render, Claude pulled a frame from every 2 seconds of the video and looked at all of them, like a very patient editor.

It caught real mistakes. One red circle sat on the wrong bar of a chart, because the website had shifted its layout after the screenshot. Another section sat empty for 8 seconds. Both got fixed before I ever saw the video.

On a second video, it caught something worse: a screenshot that showed numbers we hadn't been able to verify. It cropped them out. As someone whose job is getting details right for a client, that's the part that won me over.

Is It Free?

Not completely. Claude Code needs a paid Claude plan. The voice model is free, and the thumbnail image costs a little through Google.

Time is the bigger cost. This took most of a day, with me reviewing at every step. It's still a fraction of what one person would need to research, write, voice, animate, and edit the same video by hand.

What This Means for VAs

If you manage YouTube for a client, this changes what you can offer. You don't need to be a video editor to turn a script and real sources into a clean explainer, a Short, or a slide-style video.

But notice who made the decisions. Claude did the heavy lifting, while I picked the topic, approved the script, chose the look, and said no when something felt off. That's still VA work. It's just a bigger version of it.

Start small. Ask Claude to turn one of your client's blog posts into a 30-second Short script, and see how much of the rest you'd want to hand over.

If you try it, tell me how it goes on the comments.

Putting Jev to work: a router that decides where your prompt goes

2026-09-22T16:29:43.118+08:00

The first two Jev videos were about what the thing is. This one builds something with it — a model router you run on your own machine.

The setup is a Next.js UI talking to a local FastAPI server. Every prompt goes to Jev first, and one call asks three questions at once against the same state:

choice — what kind of task is this? Chit-chat, simple question, rewrite, code, reasoning, image.
score — how much model capability does a good answer actually need? Trivial ("a 2B model handles this") through to frontier.
noul — does this contain personal, client, financial, medical or credential data?

Because all three evaluate in parallel, asking three costs about the same time as asking one. What comes back is just numbers, so the routing is ordinary if/else. Chit-chat goes to MiniCPM5 2B running locally. Code goes out to DeepSeek V4.1 Flash on OpenRouter. An image request goes to Qwen Image 2.1, also local. Privacy above 0.5 and the whole thing stays on the machine regardless of everything else. Hard work escalates a tier — Opus 5 if it's switched on. And when confidence comes back low, the router doesn't guess, it falls back to a safe default.

The numbers are the interesting part. A "how's it going?" prompt was judged in 331ms, difficulty zero, routed local. Drop in a fake API key and privacy comes back at 0.96 and it never leaves the machine. Over a session of 53 requests, 75% were answered locally — and a full run of demos cost under one cent.

There's an honest flaw he flags before building: if you send the prompt to TypeSafe to ask whether it's private, you've already leaked it. So the second half swaps Jev out for SemIf running locally. Same decisions, same shape, 88ms, nothing leaves the box.

That's the real takeaway. Once the decision layer costs almost nothing and answers in under 100ms, you can rethink how agentic systems are structured — put a fast gate in front of everything and only spend real money when the gate says you have to.

SemIf — https://github.com/TheoLeeCJ/SemIf

Bespoke Nimble 9B — https://huggingface.co/bespokelabs/Bespoke-Nimble-9B

Decider — https://github.com/Mapika/decider

Alex Wortega's OpenJev — https://huggingface.co/AlexWortega/openjev

DiffusionGemma (vLLM PR) — https://github.com/vllm-project/vllm/pull/57250

NanoJev — https://github.com/TianyuCodings/NanoJev

Layla — https://github.com/NandhaKishorM/laya

Jev, and the week everyone cloned it

2026-09-22T16:27:51.609+08:00

For two years every frontier lab has been pushing in the same direction: reasoning. Longer chains of thought, bigger thinking budgets, models that will happily sit there for minutes before answering — and won't show you the chain of thought you paid for.

That's System 2 thinking, in Kahneman's terms. Slow, deliberate, effortful. But most decisions that actually sit inside software aren't System 2 problems. What kind of support ticket is this? Is this urgent? Did the agent's output break a rule? Those are gut calls. You shouldn't need 30 seconds for them, let alone two minutes.

A lab called TypeSafe AI just came out of stealth going the opposite way, with System One models and a model called Jev. You can't chat with it at all. It doesn't generate text. The founder, Diogo Almeida, was a lead author on the InstructGPT paper that made models good at talking to people, and his argument now is that chat is the wrong interface for software. Software doesn't want a paragraph. It wants a value it can use.

So you pass in a state — a ticket, a log, an agent trace — plus typed questions. Only three kinds: choice (pick one of these options), score (rate on my scale), and noul (yes/no, with a probability). What comes back is typed answers with real probabilities, in 70–500ms. Output tokens are free; input is four cents per million. Think of it as an if statement that understands language.

The honest caveats: there's no paper and no architecture diagram — just three hints (new architecture, parallel sampler, and RLCD, reinforcement learning for calibrated decisions). "Can't hallucinate" really means it can't break your schema. It can still be wrong.

Then the interesting part. Within 24 hours of that video, open replications started appearing — now 20 to 30 of them, and some are close. On JevBench, Jev scores 75.3 and SemIf — a frozen Qwen 3.5 4B with no training at all — sits right behind it. Everyone converged on the same trick: read the logits over the answer tokens and softmax, no sampling.

The gap is still real on hard, multi-hop work. The move there is a cascade — fast model first, escalate only when confidence drops.

How to Cut Your Fable Bill by 50–90% (Lessons From Someone Who Burned $1,486 in 4 Hours)

2026-07-04T22:34:50.423+08:00

Fable is back, it's brilliant, and it will happily drain your wallet. One creator spent over $1,400 in the first four hours after release — deliberately — to stress-test which token-reduction strategies actually work. I went through the whole breakdown, and the good news is most of the savings come from a handful of simple habits. Here's what stuck with me.

The core insight running through all of it: token management is context management. Almost every dollar wasted goes to Claude reading things it didn't need to read.

1. Minify tool output (RTK)

Claude Code runs tool calls constantly — hundreds or thousands per session — and the raw output is full of repeated noise: stdout, stdout, stdout, fixture setup lines, boilerplate. A tool called RTK (Rust Token Killer) strips and reformats tool inputs/outputs down to what the model actually needs. In the demo, one tool call went from 612 lines (36,700 characters) to 4 lines (177 characters) — a 99% reduction on that call. Not every call is that bloated, but across a real session you can expect 30–50% savings with zero quality loss.

2. Semantically compress your CLAUDE.md

Most system prompts and memory files are padded with pleasantries that carry zero information. "Hello, thank you so much for helping out with this project, we really appreciate..." compresses to "Project instructions and guidelines." Same meaning, fraction of the tokens. The demo took a CLAUDE.md from 865 words to 211 — roughly 1,125 tokens down to 274. Run this pass on every project's system prompt and memory files. Bonus: tighter instructions often produce better output, not just cheaper output.

3. Never let Claude read logs raw

If Claude needs to dig through a 5,000-line log file, don't let it read the file. Load the log into SQLite and give Claude a small query script instead. It finds the root cause — with exact line numbers — by running a query rather than eating thousands of lines of text. The same principle applies to CSVs and Google Sheets: query, don't read.

4. Block huge reads generally

Some files are just too big to read start to finish, and most never needed to be. A good pattern: have Claude sample the beginning and end of a large file, infer the structure, then use sed, grep, or a search function to jump straight to the relevant region. Instead of 20,000 lines, it reads 20 or 30. On large-resource tool calls, that's ~99% saved.

5. Prompt in English

English is unusually information-dense per token. The same query cost 51 tokens in English, 87 in Italian, 118 in German, 74 in Japanese. (Mandarin is a notable exception — symbolic characters are token-efficient.) Most models are also trained predominantly on English, so you're not trading quality away. If you work in another language, this alone can be a 20–80% swing.

6. Add context frugality rules

Bake frugality directly into your CLAUDE.md: read only files directly relevant to the task, ask before expanding beyond three files, prefer glob/grep to locate then read the region, never read generated files or fixtures unless asked. One caveat — this is the only tactic on the list that can hurt quality, because you're constraining where the model looks. The trade: be more specific in your prompts, pay less.

7. Audit your context regularly

Run /context periodically. Things creep in silently — the creator discovered he was running a dozen Chrome MCP instances simultaneously, each loaded with full context, bloating every request and confusing the model. System prompt, tools, memory files, and skills can quietly eat 8%+ of your window before you've typed a word. A neat trick: set up a looping watcher instance that snapshots your context daily and flags anything new.

8. Cap your thinking

Adaptive thinking tends to overspend. In a head-to-head bug-hunt test, low thinking found the bug in 7 turns and 1,028 output tokens; extra-high found the same bug in 9 turns and 1,363 tokens — typically closer to 1.5x over repeated runs. With a model as capable as Fable, default to low thinking and opt into big budgets only when a task demonstrably needs them. That's a 30–40% saving on its own.

Agent Loops, Explained Simply (And Why They're Not Magic)

2026-06-21T21:00:00.000+08:00

There's a lot of buzz online lately about "agent loops" and "loop engineering," with some people claiming you shouldn't even prompt your AI anymore — you should build systems that prompt it for you. A recent video does a nice job cutting through the hype and explaining what this actually means. Here's a plain-English version, plus an honest take on what's useful and where the hype gets ahead of reality.

What's an agent loop?

Strip away the jargon and a loop is simple. Instead of asking the AI to do something once and accepting whatever comes back, you let it repeat a cycle: reason (figure out what to do), act (do it), and observe (check the result). It keeps going — adjusting each time — until it hits a goal you've defined, then stops and tells you it's done.

The video compares it to a smart intern you don't micromanage. You hand them a goal and a clear definition of "finished," and they figure out the steps, check their own work, and only come back when it's actually done.

A loop really comes down to three things: a trigger (what starts it), an action (what it does), and a stop condition (how it knows to quit). And two pillars matter most — a clear goal and a way to verify progress.

Why this is genuinely useful

The big insight is that AI rarely nails something on the first try. Normally you would look at the output, give feedback, and ask for changes — over and over until it's good enough. A loop just hands that tedious back-and-forth to the AI itself, so it arrives much closer to "good" before it ever shows you the result.

In the video, the creator builds things this way: thumbnails scored against a rubric, a 3D plane that the AI repeatedly renders and inspects, even video edits where the AI cuts pauses and checks its own timing. The magic of "I did this in one prompt" is usually just a well-designed loop with verification baked in.

Where the hype gets ahead of reality

Here's the refreshing part of the video: most tasks don't need elaborate loops. You don't need five agents running 24/7 commanding other agents. The creator admits that for his own knowledge work, a single agent with a good prompt does the job most of the time, and that round-the-clock "swarms" mostly make sense for full-time coders building software.

The honest takeaways worth remembering:

Loops aren't meant to deliver 100% perfect output — they just get you much closer, faster. In the video, the AI's attempt to recreate a famous photo in code still came out looking nothing like the original.
A loop is only as good as its "done" check. Vague criteria like "until you're satisfied" lead to vague results; the best loops aim for something measurable.
Just because an expert online does it doesn't mean you should. AI shows up differently in every job — staying informed isn't the same as copying someone else's setup.

The part that stays human

Notice what you still own in all of this. You define the goal, and you decide how "done" gets verified. The video is blunt that AI is never perfect and a loop without a good verification check just produces polished-looking mistakes faster. Some runs even went 12 hours and weren't useful.

So the human role doesn't disappear — it moves up a level. Instead of correcting every draft, you're designing clear goals, sensible stop conditions, and real checks — and reviewing the final result before trusting it.

The bottom line

Agent loops are a genuinely smart idea: let the AI handle its own feedback-and-iterate cycle so you get better results sooner. But they're a tool, not a miracle. Start simple, define "done" clearly, and keep checking the work — because a loop will happily repeat a mistake until you tell it not to.

Anthropic Just Made Claude Agents Boring. That's the Whole Point.

2026-06-21T17:13:06.418+08:00

The flashy AI announcements get the headlines — new model, higher benchmark, longer context. But if you've ever tried to actually deploy an agent inside a company with a security team, you know the model was never the hard part. The hard part is the question every CISO asks in the first meeting: how does this thing touch our systems without becoming the breach we read about next quarter?

On June 18, Anthropic answered the last open piece of that question. And the answer is delightfully unglamorous.

The news: identity finally caught up

Anthropic shipped Enterprise-Managed Authorization for Claude's MCP connectors. In plain terms: an admin provisions a connector once through the company's identity provider, and every employee inherits access automatically on first login. No individual OAuth consent screens. No "click allow" forty thousand times across the org. When someone leaves or changes roles, their connector access gets revoked alongside every other app — because it's governed by the same identity rules.

Okta is the first identity provider, using its Cross App Access plumbing. The connectors live at launch: Asana, Atlassian, Canva, Figma, Granola, Linear, and Supabase, with Slack rolling out. It's in beta, Team and Enterprise plans only.

If you've never run IT for a large org, this sounds like a footnote. If you have, you know it's the difference between "we piloted it with five people" and "it's live for the whole company."

The two pieces this builds on

Here's the context most of the recap posts are getting wrong: this isn't a standalone drop. It's the capstone on two features Anthropic shipped a month earlier, on May 19. Together the three are the actual enterprise story.

Self-hosted sandboxes (public beta) moved tool execution out of Anthropic's infrastructure and into an environment you control — your own infra, or a managed provider like Cloudflare, Daytona, Modal, or Vercel. The clever bit is the split: the agent loop that handles orchestration, context, and error recovery stays on Anthropic's side, but the code actually runs inside your perimeter. Your files don't leave. Your network policies and audit logging already apply. You set the compute.

MCP tunnels (research preview) solved the other half. Your agents can now reach MCP servers sitting inside your private network without exposing them to the public internet. A lightweight gateway you deploy makes a single outbound connection — no inbound firewall rules, no public endpoint, encrypted end to end. Your internal Postgres, your private APIs, your ticketing system become tools the agent can call, and none of them ever face the open web.

Why the trio matters

Line them up and you can see the strategy. The classic enterprise objection to AI agents has always been some version of "we can't let an external service into our internal systems." Anthropic just dismantled that objection at three different layers at once.

Tunnels mean no public endpoint and no VPN exceptions. Sandboxes mean code execution never leaves your walls. And enterprise-managed auth means access is provisioned and revoked through the identity system you already trust. Each one removes a specific veto that a security team can throw. Stack them and the "no" gets very hard to justify.

That's the real shift here. The bottleneck for enterprise AI was never reasoning quality. It was governance, and governance is exactly what this release is about.

The honest caveats

I'd be doing you a disservice if I made this sound finished. MCP tunnels is still a research preview — you request access, it's not broadly available. Self-hosted sandboxes is public beta, which means it's real but you should expect rough edges. And the enterprise-managed auth is beta, Team and Enterprise only, with Okta as the sole identity provider for now. If your stack runs on a different IdP, you're waiting.

So this is a direction, not a finished product. But it's the right direction, and it's further along than anything comparable from the other labs.

The takeaway

This release won't trend the way a new model does. There's no benchmark to screenshot. But if your job is getting agents past a security review and into production, this is the most important thing Anthropic has shipped this quarter. They made the deployment story boring — predictable, governable, auditable — and boring is precisely what enterprise buyers have been waiting for.

The model was always good enough. Now the plumbing is catching up.

Building an Affiliate Marketing Business with AI: An Honest, Friendly Look

2026-06-20T21:00:00.000+08:00

There's a video making the rounds where someone claims to build an entire affiliate marketing business in about an hour — a website, Pinterest pins, an email system, even the emails themselves — using Claude plus an AI tool called GenSpark. It looks almost magical. So is it real, and should you try it?

Here's a plain-English take on what's genuinely great about the idea, what's harder than it looks, and the one habit you can't skip.

The idea in a nutshell

Affiliate marketing just means promoting someone else's product and earning a commission when people buy through your link. The video's plan is simple: pick a niche (say, kitchen gadgets), build a clean website with AI, add an email signup with a free guide, and create eye-catching Pinterest pins that send curious people to your site. AI does most of the heavy lifting — writing, designing, and even building the website from a single prompt.

What's genuinely good about it

The biggest win is speed. Things that used to take days — designing a website, writing emails, making pins in Canva — can now come back in minutes. For someone starting out with no budget for a designer or developer, that's a real head start.

It's also more approachable than ever. You describe what you want in normal language and watch the website build itself, with no code to touch. And the underlying strategy is sound: sending people to your own site and capturing emails (so you "own" your audience) is smarter than dropping raw affiliate links on social media and hoping.

Finally, it's easy to experiment. Once you've built one funnel, you can repeat it across niches and see what sticks.

What's harder than the video makes it sound

A polished website is the easy 10%. The hard 90% is getting actual people to visit — and that part the video mostly skips. Pinterest, traffic, and steady sales take time, consistency, and a bit of luck. Most affiliate sites earn little or nothing for a long while.

There are also rules you have to follow, not optional extras. Amazon and other programs require you to clearly disclose that your links are affiliate links, and they have strict terms you can get banned for breaking. AI won't handle that compliance for you.

And be skeptical of the "people are making money with this" framing. Real money is possible, but these videos rarely show the failures, the months of effort, or the fact that the easiest person to make money is often the one selling you the tools.

The rule you can't skip: check everything yourself

This is the part to underline. AI makes mistakes, and a human always needs to review the work before it goes live.

AI will confidently invent product details, quote wrong prices, recommend items that are out of stock, or write claims about a product that simply aren't true — and it sounds just as sure when it's wrong. In affiliate marketing, that's not just embarrassing; misleading claims can break platform rules or even consumer-protection laws.

So treat every output as a first draft. Before anything is published, verify each product, price, and link is real and current, read every email and pin for accuracy and honest claims, and make sure your affiliate disclosures are clearly visible. You are the editor and the one responsible for what your audience sees — not the AI.

The bottom line

The tools really can collapse hours of work into minutes, and that's exciting, especially if you're not technical. But building the site is the beginning, not the business. Go in with realistic expectations, follow the disclosure rules, and keep a human firmly in the loop. AI can do the building — you do the checking.

Can You Really Build a Whole AI Marketing Team? A Friendly Look at the Idea

2026-06-20T12:18:23.447+08:00

There's a popular video going around that promises something pretty wild: turn Claude into a full marketing team — five "agents" and a dozen "skills" — all working together to research, write, design, and even build landing pages for you. And the best part of the pitch? "Even if you're not technical, let's go."

It's a genuinely exciting idea. But before you dive in, here's an honest, plain-English take on what's great about it, what's tricky, and the one rule you should never skip.

What's the big idea?

Think of it like hiring a small team that never sleeps. You give the AI some "skills" (reusable instructions for tasks you do all the time, like making a branded slide deck or writing a blog post) and a few "agents" (specialists, like a data analyst or a content writer). Then you hand it a job — "launch our summer campaign" — and it produces research, social posts, images, and a landing page, mostly on its own.

In the video, it works impressively well. The decks follow the brand template, the images match the style, and the whole package looks professional.

What's genuinely good about this

The most appealing part is the time saved. Tasks that used to eat a whole afternoon — drafting posts, pulling a report together, mocking up a deck — can come back in minutes. For a small business or a solo marketer, that's a real gift.

It's also more approachable than it used to be. You're mostly talking to the AI in normal language, not writing code. And the idea of building reusable "skills" is smart: you teach it your style once, and it remembers. That consistency is hard to get when you're rushing.

Finally, it lowers the barrier to trying things. Want three versions of a campaign to compare? You can have them quickly, then pick what actually works.

What's not so easy (especially if you're non-technical)

Here's the honest part. The video says "even if you're not technical," but the setup involves downloading VS Code, installing tools, connecting "MCPs," and editing configuration files. That's a fair bit more technical than the friendly framing suggests. None of it is impossible to learn, but expect a real learning curve, not a five-minute setup.

There's also a cost to maintaining all this. Skills and agents need updating as your brand and goals change. And the more you pile on, the more you have to keep organized.

The rule you should never skip: check everything

This is the most important point, so I'll say it plainly. AI makes mistakes, and a human always needs to check the work.

Even in the video, the creator admits the result is "90% done" and that some charts still need fixing. That last 10% matters enormously. AI can confidently state facts that are wrong, invent statistics, misquote a source, or get your pricing or product details subtly off. It won't always tell you when it's unsure — it often sounds just as confident when it's wrong as when it's right.

So treat every output as a first draft, never a finished product. Before anything goes public:

Fact-check the claims and numbers against a trusted source.
Read it for tone and accuracy — does it actually sound like your brand, and is everything true?
Double-check names, prices, links, and dates, which AI gets wrong surprisingly often.

You are the editor-in-chief. The AI is a fast, tireless assistant — not a replacement for your judgment.

The bottom line

Building an "AI marketing team" is a powerful idea, and the tools really can save you hours. If you're non-technical, go in knowing the setup is more involved than it looks, and start small with one or two simple skills.

Most of all, keep a human in the loop. AI can do the heavy lifting, but a person should always have the final read before anything reaches your audience.

I'm Stealing This AI Researcher's Workflow for My Own Projects

2026-04-05T10:39:00.002+08:00

Karpathy doesn't use a fancy app to manage his research. He uses a folder, Obsidian, and an AI — and I want to copy it.

He posted about it last week. The short version: he dumps raw material — articles, notes, papers, images — into a folder, then lets a large language model (LLM — the AI brain behind tools like Claude or ChatGPT) build a wiki from it automatically. The LLM writes the summaries, creates the links between ideas, organizes everything into categories. He barely touches the wiki himself. When it gets big enough, he asks it questions and gets answers pulled from his own research.

I've been sitting with this for a few days, thinking about what it would look like for my work.

---

What My Work Actually Looks Like

I build things. Agents, content apps, Claude Code workflows, automation scripts. A lot of what I do involves figuring something out — what tool does what, how to wire two things together, what prompt pattern produces the right output, what broke last time and why.

Most of that knowledge lives in my head, or in scattered notes, or in past conversations I can't find anymore.

That's the problem. Every time I start something new, I spend time re-learning things I already know. What flags to use in Claude Code. What agent structure works for what kind of task. What API response format caused everything to break last month.

Karpathy's idea is simple: stop keeping that knowledge in your head. Dump it in a folder. Let the AI organize it. Ask it back when you need it.

—

The Specific Thing I Keep Thinking About

He mentioned that his wiki grows and gets more useful with every question he asks. He asks something, the AI goes through his notes and answers it — and then he saves that answer back into the wiki. So every session adds something. Nothing gets lost.

That hit me because the opposite is true for how I work right now. Every build session ends and most of the small things I figured out just disappear. The next session starts almost from scratch on some of the same ground.

If I had a knowledge base for my Claude Code workflows alone — prompts that worked, structures that didn't, patterns I figured out, error fixes — and an AI that could surface the right piece when I needed it, I'd stop repeating myself.

—

The Part That Actually Excited Me

He also runs "health checks" on his wiki. He asks the AI to find gaps, spot inconsistencies, and find connections between ideas he hadn't noticed yet. The AI suggests new things to look into.

That's the part I can't stop thinking about.

Not just a system that stores what I know. A system that notices what's missing. For someone building content automation apps, that means the system isn't just remembering what tools I've used — it's noticing when two things I built separately could be connected. It's pointing to the next piece.

That changes how building feels. Less like starting from zero every time, more like picking up a thread.

—

What I'm Going to Test

I'm starting with one folder. My Claude Code workflows — the scripts, prompts, notes, fixes, things that broke and how I solved them.

I'll ask Claude to read through everything and build an index: summaries of each file, links between related ideas, a map of what I already know.

From there, I'll ask it questions mid-project. "What pattern did I use last time for a multi-step agent?" "What was the issue I kept hitting with streaming output?" Instead of digging through old files or trying to remember, I just ask.

I'm not building the full Karpathy setup yet. I'm testing whether the core idea holds: does having a searchable, AI-organized version of my own work actually save time and reduce the re-learning?

I Told an AI to Build a Full Newsletter. One Sentence. No Code.

2026-03-14T17:12:00.004+08:00

I used to spend half a day on client newsletters. Last week I did it in 15 minutes — and I'll show you exactly what I typed.

🤖 What Claude Code Actually Is

Claude Code is an extension you install inside VS Code — a free code editor. Once it's set up, you have an AI agent sitting inside your project that can read files, write code, call APIs, fix its own errors, and run automations — all through a chat window.

You talk to it. It builds things. That's the whole loop. You don't need to know how to code. You need to know what you want.

🧪 What I Actually Tested

I set up a project, gave the agent a brand logo and a color guide, connected a few API keys (one for research, one for Gmail), and typed one prompt. That was it.

What came back:

Research pulled from live sources
Three branded infographics generated with AI images
Full HTML newsletter formatted to the brand's colors and fonts
Email sent directly to a list via Gmail
Sources linked at the bottom, clickable

It hit errors along the way. An API endpoint had changed. A Gmail formatting issue broke the layout. Both times, it found the problem, explained what happened, fixed the tool, and kept running. I didn't touch anything.

🏗️ How the Whole Thing Is Organized

The system behind this is called

Component	What It Is
Pipeline	The instructions, written in plain English — like a step-by-step recipe
Tools	The specific actions the agent can take (research, generate image, send email)
Orchestrator	Claude Code itself, reading the pipeline and running each tool in order

You also give it a file called CLAUDE.md — a standing brief that tells the agent what the project is, where things live, and what it's supposed to do. Every time you open the project, it reads that first.

Once you've built and tested a pipeline, you can schedule it to run on its own — every Monday morning, every time a form is submitted, whatever you need. At that point the agent isn't running live. The code it built is. It runs predictably, like any other automation.

💼 What This Means for VAs and Small Business Owners

You don't need to learn to code. You need to be able to describe a process clearly.

If you manage newsletters, reports, client updates, or social content for clients — build a pipeline, test it once, let it run. If you spend hours every week on the same repetitive tasks in your own business, same idea.

What makes this different from something like Zapier is what happens during the build. The agent adapts. It hits an error, investigates, fixes the tool, and updates the instructions so the same thing doesn't break again. The final automation is more reliable — and you got there faster than mapping every node by hand.

💰 If You're Thinking About Offering This as a Service

Businesses aren't paying for a pipeline or a blueprint. They're paying for a solved problem.

Don't open with "do you want AI automation?" Ask: where are you losing the most time or money right now? Find that, build the thing that fixes it, and price it on what it's worth — hours saved, errors cut, costs gone — not on how long you spent building it.

A pipeline that saves a client 20 hours a week isn't a half-day job. Over a year, that's tens of thousands of dollars in time they're not paying someone else to cover. Price it that way.

🚀 Where to Start


Tool	Claude Code — runs inside VS Code (free to download)
Cost	Paid Claude subscription — $17/month on the Pro plan
Setup time	~1 hour, most of it connecting API keys

Pick one task you do every single week. Something repetitive, something predictable. Let the agent build the first version, watch it run, fix what doesn't land, and build from there.

The hardest part isn't the tool. It's deciding what to automate first.