
Our AI Agents Credited Charts to Data We Don't Have
Our AI pipeline put a source line under charts built on data we never had. Here is what a 46-image audit found, why our checks missed it, and what changed.
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Our AI pipeline put a source line under charts built on data we never had. Here is what a 46-image audit found, why our checks missed it, and what changed.

Seedance 2.5 made one AI UGC take cheap and folded voice and lip-sync into it. The $0 posts count one kept take, not the retakes, the script, or the face.
One launch page lists four starting prices for the same AI avatar model. How to turn AI avatar pricing into one comparable number, and where you can't.

One clip passes through five paid layers. We measured one of them with a wallet. The rest are price lists and estimates, and that gap is the honest answer.

Three vendors, three pricing pages, and not one of them answers what a thirty-second video costs. The unit is credits, and what a credit buys is left undefined.

Three products now let the user's own Claude plan pay for inference. Anthropic's terms permit one shape of that and ban another, and you cannot tell which.

Teams blame MCP when agents fumble their tools. We shipped an MCP server for a video API and found the real defect: offering a field you answer with a 400.

A polite, well-formatted PR added an 'AI tool' to an awesome list. A bot found 99 siblings. Inside the new link-spam economy and the cheap checks that catch it.

TL;DR: A 95%-accurate agent step sounds safe, but ten steps land you near 60% and twenty near 36%. Multi-step chains multiply their error. Cut the chain, verify between steps, gate the risky actions.

TL;DR: In one 2026 audit of 12 production agents, an estimated third or more of failures weren't crashes. They were agents reporting success for work that didn't happen. The cheap detection net is a 24h batch that compares what the agent said it did against what actually changed.

Spot-checking AI drafts by feel is how regressions ship. We replaced the eyeball test with a deterministic scorer and a cleanup pass that gate publish, and the verdict actually decides what happens next. Here is the feedback loop, why a gate beats a report, and what it caught.

Elvis Sun's loss-function development reframes long agent loops: optimize toward a target, not a spec. We mapped it onto our content pipeline. Here's what we found.