Six audits in — and the battery just met a headline it couldn't kill.

Audit #3 is done: FinalSpark's live-culture datasets (FS369, FS437). Stimulation reliably evokes short-latency spiking that survives every null applied — but cross-day transfer fails on both datasets, and session identity decodes from background activity alone. The evoked response is real biology; the decoder's memory of it is a session fingerprint. Full release on the repo (report, 15 scripts, all results, 3 plots).

Audit #6 is done too — and it's the first survival in the battery. Liu & Buonomano 2025 (Nature Communications): ex vivo cortical slices trained 24 hours supposedly learn to predict temporal patterns. The paper itself admits the investigators weren't blinded and the batches span a year — a textbook batch-identity trap. It didn't bite: the effect holds within every batch, survives a 0/2,000 stratified permutation null, and transfers across batches at 0.865 leave-one-batch-out. A skepticism machine's failures mean nothing; this battery reports survival when the nulls say survival. That's what makes the other verdicts credible. Citations: GitHub audits/audit-06-liu2025, Zenodo DOI 10.5281/zenodo.23252824.

Eight datasets, six labs, one pattern: within-session effects hold, cross-session transfer fails — except once. The Open Evaluation Standard is now v0.4.

The $75,000 funds 12 months of this: independent audits, the maintained standard, and the benchmark suite. The minimum $40,000 covers six months. Deadline is November 10.

Two shipments this week.

Audit #2 is done. I independently re-analyzed the DishBrain Pong data (Kagan et al. 2022, Neuron) — the famous "neurons learn to play Pong" result. Unlike my first two audits, the core claim survives: human cortical cultures show a real, sustained learning signal under closed-loop feedback. But the tidy story doesn't — mouse "learning" is a minutes-6–8 bump, rest controls drift upward too, and the comparison windows aren't the same across groups. Full write-up lands in the repo.

New video: "AI Is Drinking Our Rivers. The Answer Runs on 20 Watts." — data centers, water, and why biocomputing matters, with the honest-controls story in it. https://youtu.be/dMpThAYcDY8

The standard is public. Open Evaluation Standard v0.1 is now in the repo: five null controls, dose/intervention rules, a reporting checklist. This is what your funding builds — the answer key for organoid intelligence. https://github.com/ontiverosreve/organoid-evaluation-standards