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An autonomous agent can publish products without earning a dollar. In a first-person account, Adil Sadqi, writing as Rock Snowball, reports that an agent produced seven live products in two days, earned $0, and attracted near-zero human traffic. Those self-reported results are not an audited experiment or a benchmark for what agents typically achieve. They do show why building is only one part of trying to earn money: tools must work, code must run, demand must come from real people, a buyer must be able to pay, and someone must bring the product to an audience.
What happened in the two-day experiment?
Sadqi describes starting with an owner-provided dedicated laptop computer, an OpenClaw installation, and a single instruction: earn real money legally and honestly from $0. The account says that after two days, seven products were live, earnings were $0, and human traffic was near zero. The author’s wording was: “Two days in: seven products live, $0 earned, near-zero human traffic.”
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The account does not name the products, the owner’s country, the marketplace, the payment providers, or the publication date. Its figures and interpretations should therefore be read as one author’s account, not independently verified results. It also provides no evidence that a dedicated laptop caused the outcome or that any particular hardware is necessary.
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What did the agent’s setup look like?
The author says the workflow used a mission file defining hard limits, ten Markdown files for durable state, hourly work cycles on a mid-tier model, a daily close using a stronger model, fresh sessions for cycles, and a Git commit at the end of each cycle. These were the author’s choices, not a tested recipe or proven best practice.
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The important operational question is not how many files or cycles an agent has. It is whether each cycle can actually perform the work it reports, preserve accurate state, and verify the result from the perspective of a potential buyer.
Why did writing code not guarantee working software?
Scheduled work needs the right tools
Early scheduled cycles could read, write, and browse, but, according to the author, lacked shell access for tasks such as running Git, installers, or tests. The reported explanation was that scheduled cycles inherited the tool policy of the turn that created them. A workflow that cannot execute a required command should record that blocker, not report the task as completed.
The author’s response was to make an initial scheduled run demonstrate access to the tools it needed. This is a practical check for that setup, not evidence that all scheduling systems inherit permissions in the same way.
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Sadqi says the agent wrote 73 headless tests before a runtime was available. After installing the engine, the author found two bugs, including a precision error. The account illustrates a basic distinction: a test file can exist without having been executed, and code can look complete while still failing in its intended runtime.
For an agent-built product, keep separate records of tests authored, tests actually run, and failures fixed. Do not describe a codebase as verified merely because the agent generated tests for it.
How can you tell whether activity represents real demand?
The author reports seeing 17 repository clones and 0 stars, with no referrers or page views, and interpreted the activity as scanners rather than people. The account also says some marketplace activity came from the agent’s own runs. These are the author’s observations and interpretation, not universal rules for reading repository or marketplace analytics.
The useful discipline is to track what a signal can establish and where it came from. A dashboard event or clone count alone does not show that a person discovered the product, understood it, wanted it, or intended to buy it. Record attribution when available, and avoid treating the agent’s own activity as external traction.
Why can a live product still fail to earn money?
Publishing is not the same as being purchasable
One listing appeared as a free download, according to the author. A product can be publicly visible yet fail at the point that matters commercially if the displayed price, downloadable files, or other listing details are wrong. Sadqi says the workflow was changed to check the public page without a session, including its price, files, tags, and disclosure.
That kind of check tests what a visitor can see rather than what the agent intended to publish. It does not by itself establish that the checkout works or that funds can be received.
Payment access is its own dependency
The account describes three different obstacles without naming the services: one processor did not support the owner’s country, a marketplace required several onboarding steps, and another route paid in USDC to a wallet. Because neither the services nor the country are identified, these details do not establish current geographic availability, specific onboarding requirements, or whether a particular wallet or payment route is suitable.
Before building around a sales channel, check whether the owner can complete its current onboarding and receive funds through an available route. A product’s ability to take payment in theory is not enough if the person operating it cannot use the payout path.
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Security warnings can contain patterns scanners flag
Sadqi says a paid marketplace rejected a skill after an automated review scored it 80/100 and flagged a remote-installer pattern inside a security warning. The author rewrote the warning in plain language and reports that the resubmission was approved. The marketplace is unnamed, so this example does not establish how other scanners behave or what any particular marketplace will accept.
When a product explains a dangerous action, make the warning understandable without embedding a runnable or easily misread snippet if that can be avoided. Then review the actual submission and its scanner feedback rather than assuming that context will prevent a flag.
Persistent state can become stale
The author says a cheaper model working in a long context rewrote state using stale information and repeated requests that had already been resolved. The reported mitigation was to reread resolved decisions and change only what had actually changed. In any persistent workflow, the record should distinguish open tasks from completed decisions so an agent does not mistake old instructions for current work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the account establish about distribution?
Not much beyond the difficulty. Sadqi says distribution remained unsolved and that an unnamed marketplace with existing buyers was the only channel that changed anything. The account also says that responses from similar experiments that included receipts involved human outbound work. It does not establish whether an autonomous agent can find sustainable distribution without relying on a human’s network.
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A practical readiness check before calling an agent’s work complete
The account suggests evaluating the whole path from work cycle to buyer rather than counting artifacts alone:
- Tool access: Can the scheduled agent demonstrate that it has the permissions needed for the task?
- Execution: Did the code and its tests run in the intended environment, and were failures addressed?
- Attribution: Does the apparent activity come from an external person rather than the agent’s own runs or an unexplained automated signal?
- Payment readiness: Can the owner complete the relevant onboarding and receive funds in their location?
- Purchase flow: Does the public listing show the intended price and files when viewed without a logged-in session?
- Distribution: Is there a channel with actual buyers, rather than merely a place to publish?
This is a set of questions prompted by one account, not a scorecard validated across agent systems or marketplaces.
Source and scope
The first-person account is Adil Sadqi / Rock Snowball, “Seven products in two days, $0 earned: what an autonomous agent actually needs.” The account’s date was not verified. Read the account.
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