# How DataSnack works

AI does the labor. Math does the judging. A DataSnack model is a small, explicit piece of arithmetic. Agents propose changes to it every night, a replay against real data decides what is kept, and every step is on the record.

## The method

1. **Data**: published series, news signals, and the public's implied probabilities from prediction markets (Kalshi and Polymarket).
2. **Fit**: each link's parameters, from history.
3. **Replay**: the whole model on the last 60 days.
4. **Forecast**: seven days ahead, every morning.
5. **Graded**: against the value on the day, beside standing still.

Every link in a model is shown three ways: the form of the equation, the fitted parameters, and a sentence saying what it means.

## The agents

Three critics, and one judge that isn't a model. Auditor, Scout and Razor have fixed roles and public records. They argue in short turns with numbers the system fills in. They never decide: the replay does, by a rule anyone can read.

The error is the average miss seven days out: E = (1/n) Σ |ŷ(t+7) − y(t+7)|.

The verdict: a change is adopted when its replay error is at most 0.95 × the current model's on at least 20 days; watched for five days when within 1.05 ×; rejected otherwise.

## The record

Every model is compared with standing still, the honest baseline that assumes nothing changes. A model that can't beat it says so. The public models forecast seven days ahead every morning and are graded when the day arrives. The record started in November 2026; it is young, and it says so.

- Overall record and market calibration: https://datasnack.ai/accuracy.md
- Each model's own record is on its page under "The record".

## Chaining

Decisions follow chains. A policy change travels through prices, expectations and borrowing costs. A single model answers a single question; a chain multiplies each link's fitted effect and carries every link's uncertainty with it. Example: tariffs → import prices → producer prices → consumer prices → the 10-year yield → the 30-year mortgage rate, at https://datasnack.ai/hedge-lab/tariff-mortgage, with a free Excel workbook at https://datasnack.ai/site/labs/datasnack-tariff-mortgage-lab.xlsx.

## Using DataSnack from an assistant

- Plain-text versions of every public page: add `.md` to its address.
- A summary for language models: https://datasnack.ai/llms.txt
- An MCP server (streamable HTTP): https://datasnack.ai/mcp. Reading needs no account. Signed in (the assistant opens DataSnack's sign-in page), an assistant can follow models, cover markets and export covered models as Excel workbooks; it can't post, message or pay.