Contribute to open PKU infrastructure. It works like any other open-source project: issues, PRs, and a public benchmark that gates quality automatically.
The one problem we most need help with: for a mixed food, a fruit salad, a rice-and-carrot bowl, an estimator has to guess how much of each ingredient is in it before it can compute anything. That single guess is our largest source of error. No one has a method that reliably beats a rough guess from the label alone. That is the open problem we bring to the developer community.
It's one of four measured reliability gaps; the other three already have a known path forward. Read the reliability map
We broke "the app feels unreliable" into four specific, measurable gaps. Three have a known path forward. One is the real open problem.
The open problem, Gap 3: the recipe factor. For a mixed food, how much of each ingredient is in it? That guess drives most of the error. No known method beats a rough estimate from the label. This is the one we're asking the community to crack. Because the same problem (estimating hidden proportions from an incomplete description) shows up far beyond PKU, a solution here is reusable everywhere.
The three with a known path:
Three building blocks, standard GitHub workflow. Fork, branch, PR.
The label-parsing + recipe-factor method as a Claude Skill. Priority target: reduce non-deterministic reasoning, harden behavior under real-world conditions (weak Wi-Fi, partial labels).
A maintained low-protein reference (USDA FDC / Open Food Facts derived). Add rows with provenance, improve the ingredient-to-phe classifier, expand very-low-protein coverage.
Bluetooth gram-scale support, reverse-engineered per device. Add a new scale by following the tutorial; goal is "connect the scale you own."
You don't argue that your estimate is accurate. You measure it.
Any estimator that implements the interface can be scored against the public test set. Ground truth is computed from documented sources (USDA FoodData Central / Open Food Facts), so it's reproducible by anyone.
python benchmark/run_benchmark.py --estimator your_module.Why this matters for sustainability: quality is guaranteed by the eval, and not by the author. An app can change hands, or lose its original developer entirely, and families still know it meets the bar, because every version is scored.
Every rule and every food-list row carries a citation to authority (USDA FDC id, Open Food Facts code, or named clinician sign-off), a version, and a reviewer of record. Challenges become issues; resolutions become the audit trail.
Accuracy claims are settled by measurement against the public test set, with regression checks in CI. No number goes up without evidence.
Found a gap, a failure case, or a scale we should support? We want it. File it as an issue, or leave a note here.