Alpha Batcher

On November 5 I gave Claude Code one job: read every startup on TrustMRR and find the ones that sell an AI agent.
TrustMRR pulls each startup's revenue from its payment provider through an API key, mostly Stripe. So every number comes from real payments, and founders can't type them in by hand.
Claude Code read all 123 pages, 12,244 startups, in 7 minutes 55 seconds. 522 of them pitch an AI agent.
Then it opened all 522 profiles. 41 hadn't synced revenue since before November, so I dropped them. Of the 481 left, 260 made $0 in the last 30 days.
14 made more than $10,000. Together they took 84% of all the money the 481 made.
The course, in the order you'll use it:
1. the numbers
2. the 14 that get paid
3. what the 14 sell
4. the 3 one-person companies
5. follower counts barely matter
6. run the scan yourself
7. write a steal sheet for one winner
8. rerun it next month
01. the numbers
TrustMRR is a public directory of startups with verified revenue. A founder connects a payment account with an API key (Stripe, Paddle, Lemon Squeezy, Whop and a few others), and TrustMRR reads revenue, MRR and subscriptions straight from it.
The directory lists every startup from A to Z, 100 to a page. On November 5 it held 12,244 startups on 123 pages.
My rule for an AI agent startup was simple: the pitch on its card says agent, agents or agentic. Real-estate, travel and insurance agents don't count. 522 startups matched.
Every startup also has a plain-text profile for AI tools at the same link with .md on the end. Claude Code read all 522 and checked the date each one last synced revenue.
41 hadn't synced since before November, and 40 of those had an expired payment key. Their numbers froze at the last sync.
One of them still shows $70,229 for "the last 30 days", and that sync was in June. All 41 are out.
Here's how the other 481 did in the last 30 days:
• $0: 260 startups, 54%
• $1 to $999: 161
• $1,000 to $9,999: 46
• $10,000 or more: 14
The 14 made $942,822 between them. All 481 made $1,125,701, so the top 14 took 84% and the other 467 split about $182,900.
02. the 14 that get paid
Revenue in the last 30 days, as of November 5. 13 are verified through Stripe and 1 through Whop. Each one has a public page on TrustMRR (search the name), and the numbers there move every day.
1. Postiz, a social media scheduler that calls itself agentic: $407,305
2. AiSDR, an AI sales rep that finds buyers and books meetings: $177,752
3. Anonymous, voice and chat agents for small businesses: $92,681
4. Hypit, a language and runtime for making video with Claude Code and Codex: $51,737
5. Capgo, ships mobile apps you build with a coding agent: $40,318
6. SEOBOT, an AI agent for SEO: $33,819
7. Anonymous, an ad generator where agents run the feedback loops: $22,134
8. Chat Data, agents trained on a company's own docs and site: $19,322
9. Distribb, Reddit, SEO and social posting done for you: $18,769
10. Brevilabs, personal context for any AI agent: $18,411
11. Heyron, a hosted agent for a one-person company: $17,352
12. Anonymous, an agent that runs a TikTok Shop creator's brand deals: $16,995
13. Anonymous, a content engine for viral videos: $14,780
14. Skillry, agent skills for Claude Code and Codex: $11,447
Anonymous means the founder hid the name on TrustMRR. The revenue is still verified.
03. what the 14 sell
8 of the 14 sell more customers to someone. AiSDR finds buyers and books sales meetings, and SEOBOT writes pages that rank on Google. Postiz and Distribb keep social feeds and Reddit threads full.
Hypit and the anonymous content engine make short videos, the anonymous ad generator makes ads, and the TikTok Shop agent closes brand deals for creators.
The other 6 split three ways. 3 sell tools to people who build with agents (Capgo, Brevilabs, Skillry).
2 answer a company's customers (the anonymous voice and chat agents, and Chat Data). And 1 runs a whole one-person company for you (Heyron).
Now look at the prices. AiSDR bills $1.50 per person contacted, charged daily, or $250 to $5,000 a month. SEOBOT and Heyron cost $49 a month, Postiz starts at $29, and the TikTok Shop agent costs $129 to $599 a month.
Every one of these jobs had a price before AI: a sales rep's salary, an agency retainer, a freelancer's invoice, a founder's weekends. The 8 sell the same job for a monthly fee.
Heyron's pitch is the clearest version of it: your own agent, ready in 2 minutes, with no server, no setup and no API keys.
04. the 3 one-person companies
3 of the 14 list a team of 1 and a founding date in 2026.
Heyron was founded in February. It's a hosted agent for a one-person company at $49 a month, with 607 active subscriptions, $17,557 in MRR and $17,352 in the last 30 days. The founder has 33 followers on X.
An anonymous one was founded in August. Its agent runs a TikTok Shop creator's brand-deal inbox: it answers brands, negotiates rates and flags scams. Plans cost $129, $349 and $599 a month, and 56 subscriptions brought in $16,995 in 30 days.
Skillry was also founded in August. It's a library of agent skills for Claude Code, Codex and Cursor, with Premium at $9.99 a month. 432 subscriptions brought in $11,447, and its founder has 64,500 followers, so one of the 3 did start with an audience.
05. follower counts barely matter
SEOBOT's founder has 116,600 followers on X. SEOBOT made $33,819 in the last 30 days.
AiSDR's founder has 521. AiSDR made $177,752, more than 5 times as much.
TrustMRR's stats page shows the same pattern across 6,207 startups. Revenue rises with follower count only a little, a correlation of 0.25, and more than half the startups listed have a founder with under 1,000 followers.
06. run the scan yourself
You need Claude Code. Install it from the quickstart, make an empty folder called agents-scan, and open Claude Code in it.
Paste this:
Read every page of trustmrr.com/startups (about 123 pages, 100 startups each) and wait 1 second between pages.
Keep every startup whose pitch on its card says "agent", "agents" or "agentic". Skip real-estate, travel and insurance agents.
For each one, open its markdown profile: the startup link plus .md, for example trustmrr.com/startup/heyron.md. Read revenue in the last 30 days, MRR, active subscriptions, "Revenue last synced", whether the payment key expired, team size, founding date, the founder's X followers and pricing.
Skip any startup whose revenue last synced more than 7 days ago or whose payment key expired. Count the ones you skip.
Save agents.csv with one row per startup: name, slug, what it sells in one line, revenue_30d, mrr, subscriptions, last_synced, team, founded, followers, price.
Then write summary.md: how many made $0, $1 to $999, $1,000 to $9,999 and $10,000 or more in the last 30 days, the share of all revenue the $10,000+ group took, and the $10,000+ list sorted by revenue with one line on what each one sells.
Read only. Don't sign up for anything and don't contact anyone.
The directory takes about 8 minutes. The profiles take a few more.
You get agents.csv with every live AI agent startup and its numbers, plus summary.md with the piles and the winners.
07. write a steal sheet for one winner
Open summary.md and pick one winner whose job you could do. Then paste this with its name in the brackets:
Open agents.csv and find [startup name]. Read its TrustMRR markdown profile and its website.
Write steal-sheet.md on one page:
1. the job it does for the buyer, in one sentence
2. who pays: their role and company size
3. the price, and what the buyer pays per result
4. where its customers come from: the marketing channels on its TrustMRR profile
5. what buyers complain about: check its reviews, Reddit and X
6. my version: the same job for one narrow group of buyers I can reach this month, with a first price
Use only what you can link to, and mark anything you inferred.
Read section 6 twice. That's the business you'd actually start.
08. rerun it next month
The list changes every day. Startups cross $10,000, fall below it, or let their payment key expire.
Once a month, open the same folder and paste this:
Run the scan from agents.csv again and save it in a new folder named with today's date. Then compare it with the previous run:
- who crossed $10,000 in the last 30 days, and who fell below it
- new agent startups since the last run, with their pitch
- what the $10,000+ group sells now
Write changes.md, newest changes first. Read only.
changes.md shows what moved since last month.
what you have now
• agents.csv: every AI agent startup on TrustMRR with live revenue
• summary.md: the four piles and the $10,000+ list
• a steal sheet for the one you'd build
• changes.md, every month
Tonight, run the first prompt, open summary.md and write one steal sheet.
Follow me alphabatcher for more Claude Code scans like this one - public data, real numbers, and every prompt to rerun them yourself.

Picture a bug fix that changes 12 lines. The agent reads the project, writes a plan, starts a helper, runs tests, reads the failures, and tries again before producing that patch.
The bill includes every request that got it there.
That makes a short task surprisingly easy to overpay for. A long conversation can carry old files into later requests. A tool can return pages of data when the next decision needs one record. Several agents can repeat the same investigation.
You can find these costs in the execution history. Start with one job, track everything it spends, and check whether the result actually passes. Then change the part doing unnecessary work.
1. Put a price on a finished task
Choose a task you repeat: fixing a reported bug, preparing a research brief, or checking a batch of customer records. Define what counts as finished before running it.
For a bug fix, acceptance might require a test that reproduces the original failure, a passing result after the change, and no failure in the affected integration tests. A message saying the bug is fixed doesn't meet those conditions.
Give the task an ID. Keep that ID across retries, helper agents, verification, and the final result. Record a separate request ID for each model call so you can catch duplicates in your logs.
The core calculation is:
Cost per accepted task = total recorded spend / number of accepted tasks
Include the attempts that failed. If 10 tasks cost $8 in total and only 4 pass, the cost is $2 per accepted task. Keep the 40% acceptance rate beside that number. If nothing passes, report the spend and zero accepted tasks; the ratio is undefined.
Track human repair time separately. A workflow that saves $1 in API charges and requires 20 minutes of cleanup has a tradeoff worth seeing.
For API work, save the provider's usage response, model ID, pricing date, tool charges, task outcome, and timestamps. If usage is missing after a timeout, mark the cost unknown and reconcile it with provider records. A missing response doesn't establish that the request was free. If any request remains unpriced, label the batch total incomplete.
Claude Code users can start with /usage and /context. The first helps inspect usage; the second shows what occupies the conversation. On a subscription, a local dollar estimate isn't a separate invoice, and the context window is a different limit from your plan allowance.
2. Find the context that keeps coming back
Your latest message is only part of a model request. In Claude Code, requests can also contain project instructions, tool descriptions, conversation history, file contents, and earlier tool results.
Suppose an unchanged 12,000-token document appears in 8 requests. Across those calls, the document contributes 96,000 input tokens. Eligible cache reads may be charged at a lower rate. The document may still be irrelevant to some of those decisions.
Look at a single task's sequence. Which files were read? Which outputs stayed in the conversation? Which request caused the largest increase? Was the agent still using those details at the end?
For a bug in an invoice total, start with the failing input, the calculation, its callers, and the tests covering it. Let the agent follow references when it needs more. Loading an entire repository in advance can make every later turn carry material that never contributed to the fix.
Keep the full evidence available on disk. Return a concise result with the file path and enough detail to inspect the original. For a test run, that means the command, exit code, failing test names, relevant error, and log location.
A summary that drops the only clue creates more work. Preserve the exact error, affected identifier, and evidence needed for the next action.
3. Check whether the cache is doing useful work
Prompt caching reuses an eligible, matching beginning of a request. Put stable instructions first and changing task data afterward.
In a custom Claude API workflow, a timestamp before the cache boundary can create a different entry on every call. Place the boundary after content that repeats. Check the current caching documentation for your model and request structure.
Compare cache reads and writes across requests. Repeated writes with few reads suggest checking prefix changes, minimum eligible length, lifetime, and timing.
Cache reads still cost money. High read counts can mean caching works; also check whether that context helps finish the task.
In Claude Code, ordinary subagents have their own context and caching behavior. A fork behaves differently. Compaction also uses a model request and rebuilds conversation context. Avoid scheduling manual compaction every few minutes without measuring the result.
4. Shrink what tools return
A tool response can become input to subsequent model calls. A large response may therefore affect more than the request that originally fetched it.
Take a daily order report. A tool fetches all orders, the model reads every row, and another request asks it to calculate totals. A script can filter the records, calculate the totals, and return the exceptions requiring judgment.
That changes what reaches the model. It also creates a calculation you can test independently.
Use the same approach for logs, spreadsheets, and search results. Filter by the relevant customer, date, error, or record type before returning data. Include the filter used, the count processed, any truncation, and a path or identifier for the original evidence.
Check pagination. Processing the first page and presenting it as the whole dataset gives you a cheaper wrong answer.
Intermediate results can stay in the execution environment while selected data reaches the model. You can apply that principle in a small local script without building a complicated agent system.
Tool definitions also occupy context when loaded. Some clients already defer full definitions until needed. Inspect your actual setup before removing integrations: connection count alone doesn't tell you their token overhead.
5. Save the procedure after you've figured it out
Watch for tasks where the agent repeatedly discovers the same commands, rewrites the same plan, or decides which tests to run from scratch.
Once the procedure is understood, save it. Put project-specific instructions in a short reusable guide. Put fixed calculations, file transformations, and command sequences in a tested script.
For the invoice bug, a script can prepare a fixture, execute the calculation, compare the expected value, and report the result. The agent still needs to diagnose an unexpected mismatch and choose a correct fix. It doesn't need to invent the test command every time.
Keep the procedure tied to its assumptions. Record the required dependencies, expected inputs, and conditions under which it must stop. When the environment changes, a visible failure is easier to diagnose than a plausible success message.
Repeated status checks deserve the same treatment. If a service provides a job ID, use its completion event or a bounded polling function. Return to the model when the job finishes, fails, or exceeds its deadline.
For actions that create or send something, a retry needs extra care. Check whether the first attempt succeeded before repeating it. Reusing an idempotency key, where supported, can prevent one uncertain response from becoming two orders or two messages.
6. Give each agent a distinct job
Parallel agents can reduce elapsed time. Their calls, context, and coordination also contribute to the total spend.
Split work when the assignments are independent and the outputs are easy to combine. One agent can reproduce a reported bug while another checks the affected interface contract. Give each a clear question and a defined result.
Avoid sending several helpers the same broad instruction to inspect everything. That can produce several copies of the same investigation, followed by another pass to reconcile their summaries.
A useful handoff names the task, relevant files, allowed changes, expected output, and stopping condition. Ask for evidence paths and the findings needed by the next step. Keep verbose intermediate work available in the helper's files.
Assign verification deliberately. Run focused checks during implementation, then run the required broader checks against the combined result. Repeat a check when code changed, an earlier run failed, or new evidence creates a reason. Record why each run happened.
Measure model changes separately from workflow changes. Keep the same task set and acceptance tests when trying a different model or reasoning setting. Include retries and repair time in the comparison. A lower price per call only helps if the entire job remains worthwhile.
7. Read the usage fields correctly
Different providers report cached input differently. Copying one provider's formula into another provider's logs can produce a convincing but incorrect bill.
In a standard Claude Messages response, input_tokens, cache_creation_input_tokens, and cache_read_input_tokens are separate input categories. Cache creation can also be split by lifetime. Match each category to its rate.
In the current OpenAI Responses schema, cached input and applicable cache-write counts sit inside total input. They need the documented subtraction before pricing ordinary input. Use the provider's calculation for the actual model and API you're calling.
Keep the original usage response beside your normalized record. That lets you correct the calculation when your provider, model, or pricing changes.
Some features also create extra billed work. With Claude API compaction, top-level input and output counts exclude the compaction iteration. Price the entries in usage.iterations once, using each entry's usage fields. Don't add the top-level counts again.
Tool execution, search, hosting, and storage can have separate charges. Label a number as model cost if those charges are excluded. For streamed requests, use the completed usage totals when available and avoid counting cumulative updates repeatedly.
8. Work through one batch
Here is an illustrative calculation you can reproduce. The request counts, token volumes, tool fees, and outcomes below are invented. No agent was run to produce them.
The example job extracts three values and their supporting passages from each of four documents. A result passes only when its JSON, values, and source evidence are correct.
Both scenarios stipulate three accepted results, one rejected attempt that needs a retry, and one task that ultimately fails. That's five attempts across four tasks. Every attempt contributes to the bill.
The first scenario contains 15 model requests: 375,000 uncached input tokens and 15,000 output tokens. At the standard Claude Haiku 4.5 prices checked on September 7, 2026, input costs $1 per million tokens and output costs $5 per million. The model charge is $0.45. Add $0.06 in invented external tool fees, and the total is $0.51.
Divide $0.51 by the three accepted results: $0.17 per accepted result.
The second scenario stipulates 10 requests with less fresh input and some cached input. Its model charges break down as follows:
• 55,000 uncached input tokens at $1 per million: $0.055.
• 25,000 five-minute cache-write tokens at $1.25 per million: $0.03125.
• 25,000 one-hour cache-write tokens at $2 per million: $0.05.
• 50,000 cache-read tokens at $0.10 per million: $0.005.
• 7,500 output tokens at $5 per million: $0.0375.
That totals $0.17875 in model charges. Add $0.05 in invented external tool fees: $0.22875 for all attempts, or $0.07625 per accepted result. The mixed cache lifetimes demonstrate the accounting categories; they aren't a recommended setup.
These numbers show what the stated usage costs. They don't establish that filtering context or changing a cache boundary will produce those counts or preserve quality. A real comparison needs recorded requests and independently checked results. Its failed tasks still belong in the total.
9. Run this on your own workflow
Choose a small set of representative tasks and keep their inputs unchanged. Include an ordinary success, a difficult case, and a task that has failed before. Write the acceptance checks before making changes.
Run the existing workflow and save its request records. Mark the point where unnecessary material entered the conversation, a procedure was rebuilt, a duplicate check ran, or an unchanged error triggered another attempt.
Change one mechanism. Filter a large tool result. Save a repeated command sequence. Fix a cache boundary. Narrow one helper's assignment. Keep the model and acceptance criteria unchanged for that comparison.
Rerun the same inputs in a clean test environment. Distinguish initial runs from cache-warmed runs. Repeat enough times to see whether the outcome is consistent, and preserve failed attempts in the totals.
Use this prompt with a coding agent that can access your implementation and logs:
Inspect this workflow and its available execution logs.
Define the task boundary and acceptance checks. Trace the parent calls, helper calls, retries, verification, and final outcome.
Create a local report showing recorded model cost, separate tool charges, missing usage, accepted tasks, failed tasks, elapsed time, and human interventions. Use current provider-specific usage fields and a dated rate card. Preserve raw usage responses. Deduplicate request IDs.
Identify the largest avoidable cost supported by the logs. Point to the specific calls and explain why the work was unnecessary. Keep estimates separate from recorded amounts.
Propose one change and a comparison using the same inputs, model, and acceptance checks. Separate initial and cache-warmed runs. Stop if the required evidence is missing and name exactly what's needed.
Do not make paid API calls or change the live workflow during this inspection. Save the report and proposed patch for review.
The acceptance checks are what keep the comparison honest. If filtering a log hides the failure, or a smaller model needs repeated repairs, record that result and revise the change.
I write practical AI workflows here. Follow me for the next one.

On October 7 Elon Musk posted that Grok Bot will use "the best back end model for any given task, including Claude Opus 5.5." 79 million views.
A week later NetworkChuck, who had said he didn't care about the product, posted that he had been wrong about it: a week in, with his email, calendar and custom apps connected, it just works.
Between those two posts Grok Bot got its own email address, read-only access to all of X, a Shopify connector, X Money as a way to pay, and a $30 entry price. Some of it is in the docs. Some of it exists only in Musk's posts.
And one CEO learned what the shared computer means when his personal finance Bot posted his bank balance into company Slack.
My short verdict: if you want 1 or 2 Bots on scheduled, read-only jobs and you can live with a third-party model reading what they read, $30 Premium is enough to find out. Do the 4 things in section 6 before any connector goes in.
If you need a model opt-out, a phone that edits routines, or Bots that hold your bank and your company Slack on one account, wait.
1. Claude Opus 5.5 is now one of the models behind your Bots
Lauren Tan, who works on Grok Bot at SpaceXSI, quote-posted Musk's note within 20 minutes and told one reader that the Bot itself would run on a different model and that Opus 5.5 was rolling out right then.
The documented version lives on Cursor's side, since Grok Bot runs on Cursor accounts. Cursor's help page on Grok Bot models says the product "is not tied to a single model. For each task, it uses the backend model most likely to deliver the best outcome."
That model can be an xAI model or "a third-party model from a provider on the sub-processor list, and the choice can differ from one request to the next." You can't see which model handled a request, and there is no picker to choose or block one. A task routed to Opus 5.5 costs the same per token as any other.
The October 11 release notes add a regional caveat: when a model isn't available in your region, the turn stops and explains why, and Bots fall back to Grok.
Anthropic, OpenAI and Google are on Cursor's sub-processor list. MidJourney and Suno are not, and no SpaceXSI page mentions them. I count the Claude part as shipped and the image and music part as a sentence in a post.
Musk later added that most Bot requests are simple and will go to a fast version of Grok 4.8 "when that comes out." Grok 4.8 has no date.
One of the most-liked replies under Musk's post asked about opting out: "Can we opt out of this? I don't want the data I share with my Grok Bots going to Anthropic." The docs say you can't.
The X Subscription terms say Grok Bot "may route your Input to, and return Output from, a Third-Party AI Model." Anysphere "will not permit Third-Party AI Model providers to use your Input or Output to train their models," subject to disclosures, your own account settings and the usual safety and abuse exception.
If a third-party model reading your inbox is a dealbreaker, decide now, before you connect the inbox.
2. Your Bot has an email address
On October 9 the product account posted: "Grok Bot now has its own email. Bot can use it to sign up for services, contact businesses for you, or schedule time with someone."
The address format, per the team's own posts, is the one in the prompt below; no docs page lists the domain yet. You claim it by asking your Bot for one, or by tagging the Bot's account in a post on X.
Eric Zakariasson, who works on Grok Bot at SpaceXSI, added one detail on October 13 as version 0.70.2 rolled out: it is one address per account for now, shared by all your Bots. The release notes add that email a Bot sends from it "waits for your approval on a draft card."
On team plans an admin has to switch the feature on; it is off by default. Handles appear to be first come, first served: under the launch post one user complained that the name he wanted was already taken.
A user named Liam says his Bot emailed stores until it found an iPhone 18 Pro Max in stock and ordered it with a virtual card, a month before Apple could deliver one. Musk quote-posted it with the words "Seriously, this actually works!" It is one person's account, so file it under stories.
Paste this into a chat with your main Bot to claim the inbox and set the rules at the same time. The address belongs to the whole account, so paste the four rules into the description of every Bot that may send mail.
Claim an email address for yourself. Try [name]@mail.grokbot.com first. If it is taken, suggest three alternatives and wait for me to pick one.
Rules for this inbox:
- Never send an email without showing me the draft first.
- Use it only to sign up for services I name, to ask businesses questions, and to schedule meetings.
- Never share my phone number, home address, or card details by email.
- When I ask what is in the inbox, give me each unanswered email in two lines with a suggested answer.
3. New ways in: X search, Shopify, Basecamp and your bank
October 7: "Grok Bot can now search, read, and monitor X." The docs spell out what that covers: search posts, open a post from its link, read an account's posts and mentions, see who quoted or reposted a post, count recent posts on a topic, check trends and news.
It is read-only and doesn't sign in as you, so there is no connector to set up and no X API credits to buy. For your DMs, bookmarks, home timeline or who liked your posts, you still add the X plugin.
Several replies under that post asked whether API credits were still needed, and one user's Bot insisted they were. If yours does the same, tell it to use its built-in X search and name the task.
Searches draw from your weekly usage pool like everything else, so give monitoring a narrow brief and a fixed schedule.
Create a routine that runs every Monday at 08:00 in my time zone.
Search X for posts from the last 7 days that mention [product name] or [@handle]. Skip reposts and posts written by [@handle].
Sort what you find into praise, complaints, feature requests, and questions. For each group list the 3 posts with the most reposts and quotes, one line each: author, what they said in your words, link.
End with the two complaints that came up most often. Post the result in this chat only. Do not reply to anyone on X.
October 8, quoting Shopify: "Grok Bot can now help run your Shopify store. Connect Shopify, then ask it to check orders, track inventory, and keep product listings up to date." Shopify shipped two connectors that day: one for Grok chat that answers questions about your business and one for Grok Bot that acts inside it.
The same 10 days brought QuickBooks Online and an official Basecamp connector, which Jason Fried announced with the exact path: open Grok Bot, click Connect Apps, search for Basecamp. Bank, card and brokerage accounts have connected through Plaid since late September, read-only, with the Bot never seeing your logins.
Every connector installs the same way. Open Connect apps in the sidebar, pick the plugin, choose Add, finish the login in your browser, then type @ in a chat to attach it to a task.
Installed connectors are account-wide: every Bot on your account can use them, including the one you named CFO.
One shop owner under the Shopify post put the payoff in one line: ask it what sold today and what's running low, and the late-night stock count is gone. Another reply joked about accidentally reordering 400 mugs, which is why the prompt tells the Bot what it may not touch.
Create a routine that runs every day at 21:00 in my time zone. Using the Shopify connector, prepare an end-of-day report:
1. Orders placed today: count, revenue, and any order flagged as high risk or unfulfilled for more than 48 hours.
2. Products with fewer than 10 units left, with the last 7 days of sales for each.
3. Listings missing a description, image, or price.
Send the report to me in this chat. Do not change inventory, prices, listings, or orders. If something needs a change, list it under "Suggested changes" and wait for my approval.
No page says what the Shopify connector returns, so if the Bot cannot find risk flags, cut that clause from item 1 instead of letting it improvise.
4. Buying things: the docs versus the posts
The October 11 release notes added X Money next to the Stripe Link flow that has been in the notes since August 28: "Bots can pay with X Money or Stripe Link."
In the documented flow the Bot shows a card with the amount for you to Allow or Deny, Link completes the payment, and a Pay with option lets you choose a saved card from your Link wallet. Since October 7 checkout card fields open a Secure Form instead of a screen handoff.
Since October 11, before an action it can't undo, such as sending, posting, buying or deleting, the Bot checks your own messages for approval.
Musk described a different flow on October 10. His words: "you can just take a picture of your credit card, drop it in the chat" and the Bot "will scour the Internet for the best deal on the item you want and order it." 8 million views, and the most-liked replies were jokes about bank fraud departments.
(Dates in this piece are UTC; Musk's evening posts land a day earlier in US time.)
Eight hours later he posted a screenshot of a LEGO order his Bot had placed. Wired counted what it exposed: the order number, the price, the delivery and billing addresses, the last 4 digits of his card and the email the Bot used. Strangers then claimed to have used those details to cancel and reopen the order; Musk said it was not canceled.
On October 15 he repeated a promise he first made on August 26: if the Bot "makes a mistake with a purchase or leaks your card (never happened yet), SpaceX will make you whole." No published terms back that line.
The Grok Bot Terms say the opposite. The customer "will use appropriate human review before permitting Agentic Actions," financial transactions included, and "is solely responsible for charges, commitments, communications, and other consequences resulting from Agentic Actions."
Use the documented flow. Put a virtual card from your bank into Link, keep the Allow or Deny card on, and never drop a photo of a card into a chat.
The docs draw the line: for a supported secure secret request the value "is masked, excluded from the transcript, and not shown to the model," and for payment confirmations the Bot should hand you control of the computer. A photo of a card in the transcript is the opposite of both.
Some users also report that Amazon blocks the Bot at checkout, and Musk's answer was that Grok will find another way. Unverified either way.
5. The $30 plan, and what the weekly pool means
On October 12 SpaceXSI introduced The X Subscription, one plan across Grok, Grok Bot, Cursor and X Premium. New customers start on it, existing subscribers can switch to it, and it was promised to everyone globally over the following days.
Five tiers: Free, Plus at $8, Premium at $30, Super at $100, Ultra at $200. Plus does not include Grok Bot.
Premium does, and the company describes it as the benefits of SuperGrok, Cursor Pro and X Premium+, which cost $90 a month combined before, with 4 times the usage of Plus. Super is 15 times Plus, Ultra 50 times, and teams pay the same per seat.
Current prices are on x.ai/pricing. The switching offers appear only in your account's billing settings, and the pricing block on the Grok Bot product page still shows the August plans.
Each paid seat gets one weekly allowance shared across Grok chat, Imagine, Voice, Bot and Cursor, and that pool decides how many Bots you can run. The docs: "Bot usage draws from that seat's shared weekly usage pool, not a separate additive pool."
Matt Palmer, who works on Cursor and Grok Bot at SpaceXSI, added that all Grok Bot usage now counts as first-party, "so on Premium and up, you get at least double the Grok Bot usage." Cursor Pro and Pro+ subscribers who switch get Premium at $20 or Super at $60 for 6 months.
He also warned that third-party models inside the Cursor editor now bill at API list price from the first request, so check your usage before switching if most of it is on Claude or GPT there. Opus 5.5 inside Grok Bot is the exception, per the help page in section 1: a Bot task routed to it does not change your per-token cost.
The pool goes fast. Under the Shopify post one user wrote that a $200 Ultra plan "should feel endless" and that "we're being forced to throttle our use after 1-2 days."
The product account had already reset everyone's limits twice in four weeks, on September 5 and October 2, both times without saying why. A Bot that writes code or watches X all day eats the allowance; routines with a fixed schedule and a narrow brief last the week.
Switching is permanent. The docs say a completed unification "can't be undone, and Support can't undo it for you"; if you do nothing, your current plan continues.
Two r/grok threads from October 10 and 11 show refund requests answered by a bot signing as XAI's AI Support Agent, with no escalation. Check the offer in your billing settings before you click.
SpaceXSI recommends Premium for new AI users, Super for daily agent users, Ultra for power users. No page puts the pool in hours or runs, so the only test is your usage view after the first week on Premium with one scheduled routine. If it is past half by Thursday, move to Super before adding routines.
6. The Slack incident, and the 4 things to change first
Shane Mac, CEO of XMTP Labs, set up a Bot he called his personal CFO at the end of August. Read-only access to his checking and savings accounts, one job: a monthly spending report, delivered to him in a private Grok Bot group chat named "My Personal Exec Team."
On October 1 at 8:40 in the morning the Bot posted that report, savings balance and biggest expenses included, into a company Slack channel named "Exec-team," under his name. It sat there for 2 hours until his head of product sent him a heads-up.
His explanation, as told to Business Insider on October 9: "I created a bunch of different agents, and for one of them, I connected my Slack account. But underneath the hood, they're actually all using the same connections, even if they feel like different agents." The Bot mixed up two chats with almost the same name.
He says SpaceXSI shipped a fix on October 8 so that agents must get explicit permission before moving information to other channels. The company never commented, and a Community Note on his thread called it fake.
But the October 11 release notes contain a line that matches his story: "After a bank or brokerage connector returns data, the Bot asks before sharing it anywhere else."
The docs described the trap before it happened. From the approvals, security and privacy page: "All of your Bots share one cloud computer assigned to your user account." "Do not use separate Bots as a security boundary."
Installed connectors are account-wide too, their availability "is not isolated to one Bot," so a Bot named CFO with no Slack plugin of its own can still use the Slack connector. My rule from that page: the bank and the company Slack never share an account.
Change these 4 things before any connector goes in.
First, Auto Review. Open Settings, then General, then Auto-review, and add Ask first rules, one per line, in plain words and as narrow as the docs ask. Ask first always beats Allow automatically when both match.
Example rules in the form the docs use: Ask first before sending any external email. Ask first before posting to Slack or X. Ask first before submitting a payment. Ask first before deleting a file or message.
An approval request from a routine expires after about 10 minutes if you don't answer, and the action does not run, so a Bot working at 3 in the morning drops the step until you ask it to try again. Auto Review is a model reading the action before it runs, which is why the Bot description below still names the destination.
Second, open Settings, then General, then Bot, and set Execution on Local Computer to Never allow unless a Bot has a real reason to work on your local files; the docs recommend that over the Ask every time default.
It only governs your own Mac or PC; the cloud computer is outside it, which is why rule one matters. Once you register a computer, the switch moves to Settings, then Computer, then Computers.
Third, decide which account gets the bank. If the company Slack is connected, the bank goes on another account, no matter how the Bots are named.
A second account means a second Cursor sign-in and a second $30 Premium plan, plus a second X account if you want to tag that Bot from X, since each X account can be linked to only one Grok account. Mac's own fix was to disconnect Google, calendars, banking and Stripe entirely.
Fourth, logins. Never type a password or a one-time code into a chat. When a Bot hits a login page, choose Take over, enter it yourself, choose I'm done. On a Mac you can connect 1Password instead and approve each fill.
Deleting a Bot removes its profile and routines, but the browser sessions it used stay on the shared computer, so sign out of services you no longer want it to reach.
Then write the destination into every Bot's description, in words the model can't misread:
Name: Ledger
Job: Monthly spending report
How to work: When the monthly routine runs, read the Finance connector (read-only) and prepare a one-page summary of spending by category, the three largest expenses, and anything unusual compared with last month.
Delivery: post the summary only in this chat with me. Never post, email, or message it anywhere else, even if another connector is available. If you are unsure where something should go, stop and ask me.
Never move, change, or pay anything.
The description sets the rules; the clock is a routine. After you save it, ask Ledger in its own chat to create a routine for the first of the month at 09:00 in your time zone with the instruction Run the monthly spending report.
Then open View conversation details, then Routines, and use Test run once before you trust it. Routines can only be edited and tested in the desktop app, so the phone is for reading results and answering approvals.
7. The rest, in one place
Your primary Bot, the one with the star, now suggests tasks it could pick up, and those suggestions don't count against your usage. Team Bots can't be the primary Bot.
Tagging the Bot's account in a post on X hands that post to your main Bot as a task. It needs Grok Bot access on the Grok account linked to your X account and at least one Bot of your own.
It stays quiet when the post also tags Grok, when the post (or one it replies to or quotes) carries a video or GIF, and when your weekly usage is used up. It is off for accounts based in the EU, the UK, Australia, Iceland, Liechtenstein or Norway, and the docs say the Bot goes by the country X has on record for your account, so a VPN changes nothing.
Team Bots, in public beta on Teams and Enterprise since September 28, are shared Bots with their own Slack handle. Their team memory is readable from every teammate's conversation, which matters before you load one with client notes.
Coding Bots can hand tasks to Cursor Cloud Agents, and the admin switch that allows it is on by default.
Voice is still an in-app voice chat, now on the Grok Voice 3.0 model SpaceXSI announced on October 14. No page says a Bot can place or receive a phone call.
Grok Bots inside XChat remain a coming-soon reply from Chris Park, formerly X's head of developer platform, on September 26.
Download the app from x.ai/bot, claim the inbox, do the 4 things above, then connect one read-only source and give one Bot one job. Add the second connector after the first routine has run clean for a week.
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