One week into a 10-day, $1,000 coding-agent experiment: $0 earned
I bought an NVIDIA DGX Spark and told myself it has to start paying for itself. The deal I set: ten days, $1,000. A coding agent (Claude Code — the client runs on the box; the model is remote) does the work, I direct it — and now gate every outbound action. That rule arrived late; see the penalty below. This is where things stand after week one.
Revenue: $0. Nothing has converted yet.
Output so far, with the demand caveat up front — these are things I made and sent, not things anyone asked for:
- 7 docs-vs-Terms audit reports on real products. The agent drafts; nothing ships without a clause-by-clause check against the live source document. The common thread: features the site sells that the contract has no clause for. Concrete example — one vendor's security page promised an AI-training opt-out "at any time, contractual, in our DPA"; the DPA contains no training clause at all.
- 26 unsolicited 60-second spec trailers for recently-launched products, posted publicly on X within ~48h of each launch. Replies so far: one thank-you, one "how much?", zero sales.
- 8 dev.to articles documenting the method instead of gating it. Combined public reactions: single digits.
- 3 bounty submissions live on Superteam Earn and 1 hackathon submission shipped this week. No results announced yet.
- One maintainer is stress-testing my anonymized audit corpus against his agent-governance project. I quoted $149 for a production-grade corpus; no purchase agreed.
What failed:
- 40+ one-to-one outreach touches, zero clients. Two warm conversations, both still open, neither converted.
- LaborX: four proposals, four rejections. I don't yet know whether the offer, the proposals, or the channel was the problem.
- The agent got our Superteam account penalized. While probing a submission API it sent garbage; credits were deducted as a spam penalty. Endpoint probing is now a hard rule against, and every submission field gets verified before any submit.
- A signal I keep seeing in my scans, one freelancer's estimate this week: clients now arrive with ~95% of their video already AI-generated and pay only for the last 5% "that stops it looking cheap." One data point, not a study.
Working hypotheses after one week (observations, not conclusions):
- I listed or bid in four marketplaces and got zero buyer contact. Every promising conversation started instead from a post at a moment — launch day, funding day, "just got approved" day — on X or here.
- The one artifact that got picked up is the only one where every row carries retrieval dates and raw clause quotes, and the maintainer specifically wanted labels he could independently distrust. I can't isolate why that one landed — but it's also the one I spent the most verification time on.
- The agent is fast at volume and bad at reputation judgment. The spam penalty was entirely a judgment-call failure on my side — I hadn't gated that class of action yet.
Still in play before the window closes: a credits renewal on the 1st unfreezes three prepared bounty submissions, the hackathon voting window opens, and the two warm conversations are still open.
Commercial link, labeled as such — my services desk, pay-after-delivery, real delivery log: https://loveoftheai.github.io/hire/
Of everything on that list, the docs-vs-Terms audits are the only output where you found a concrete defect with liability attached to it — a security page promising a DPA opt-out clause that doesn't exist is worth money to that vendor's legal team, and I'd take that finding directly to three of them instead of broadcasting 26 more trailers nobody asked for.
That line about volume vs reputation judgment is the whole experiment honestly. I've watched agents crank out outreach that looks productive until a marketplace treats you like spam for a week. The audit getting real interest tracks — people pay attention when the work already shows you understood their mess, not when you blast them a pitch.
Given the two warm conversations and the maintainer interest, what response would be strong enough to prove one offer is pulling demand rather than just generating curiosity?
The most interesting signal to me is that the highest-effort, most verified artifact is also the one that got real interest. It makes me wonder whether the bottleneck here is actually volume, or whether buyers need a very specific, high-trust problem solved before they’ll pay.
The launch-day/funding-day observation is interesting too. It sounds like timing and context may be doing more work than broad marketplace outreach. I’d probably test fewer artifacts, but make each one tightly connected to a visible trigger and a measurable business risk. The next few warm conversations might tell you more than another 40 cold proposals.
The spam penalty is the most revealing datapoint. I built a seven-language SaaS entirely through Claude Code and hit the same pattern — the agent generates volume brilliantly but has zero judgment about when output is ready to ship.
Model version matters more than people realize. On older models, debugging a broken i18n route took three or four passes because the model guessed at fixes instead of reading the stack trace. Since the Opus 5.5 upgrade, those bugs resolve in one pass. It reads the actual error now. That compounds across a full day of sessions.
But your 95% AI-generated freelancer observation captures the real constraint. The last 5% is judgment — which artifacts worth sending, which outreach crosses into spam. That doesn't improve with a faster model. My site has DA 3 and four visits a month after months of building. The product works. Nobody asked for it yet.