# Desk log AIDB-BUILD-RERUN-20260822

**Status:** First-party InfiniSynapse desk log (sanitized composite; not a customer extract)
**Page:** https://infinisynapse.com/en/blog/ai-dashboard-generator
**Run ID:** `AIDB-BUILD-RERUN-20260822`
**Date:** 2026-08-22 (Saturday)
**Operator:** InfiniSynapse Data Team
**Attestor:** William Zhu, InfiniSynapse cofounder ([GitHub @allwefantasy](https://github.com/allwefantasy))
**Contact for contradictions:** zhuhl@infinisynapse.com

## What this file is

A downloadable record of a drawing-app canvas screenshot versus a same-goal rerun that left three charts, a Markdown exception list, and inspectable SQL. Companions: [aggregate CSV](https://infinisynapse.com/blog-media/ai-dashboard-builder/downloads/aggregate-AIDB-BUILD-RERUN-20260822.csv) and [verify script](https://infinisynapse.com/blog-media/ai-dashboard-builder/downloads/verify-AIDB-BUILD-RERUN-20260822.py). It is **not** a named-logo customer case, a vendor bake-off, or an IMF / BIS / OECD / Gartner experiment.

## Four-step method (reproducible)

1. Connect one authorized source—or two, if the meeting needs both.
2. Write the meeting goal you will reuse. Do not ask for “some charts.”
3. Open the plan and the query behind each figure.
4. Rerun the same goal next week. Confirm a teammate can download both packs.

## Source and goal

| Field | Value |
|---|---|
| Sources | Read-only Postgres replica + sanitized SKU note file |
| Goal asked twice | Wednesday stand-up: which SKUs missed promise this week |
| Contrast | Drawing canvas vs rerun pack |

## Results

| Retrieval state | Charts | Memo | Inspectable SQL | Next-week rerun |
|---|---|---|---|---|
| Drawing canvas | 0 | 0 | 0 | 0 |
| Rerun pack | 3 | 1 | 1 | 1 |

| Team | Drawing canvas (hours stale) | Rerun pack (hours stale) |
|---|---|---|
| Ops | 48 | 2 |
| Finance | 72 | 3 |
| CS | 36 | 2 |

Wall-clock for the successful run: 24 minutes (warehouse time excluded).

No independent reproduction was known as of 2026-08-29. Independent replication should disclose tool, model, version, configuration, prompts; source schema, snapshot, access; metric, grain, joins; goal, window, timezone; SQL and transforms; run IDs, status, errors, timestamps; chart, data, and artifact hashes; canvas baseline construction; all failures; review and accessibility protocol; freshness and wall-clock definitions; and conflicts of interest.

## What you may cite

- Artifact counts 0/0/0/0 → 3/1/1/1, freshness 48/72/36 → 2/3/2 hours, ~24 min wall-clock, run ID

## What you may not claim

- Customer speedup %, official EEAT score, named-logo case, or that IMF / Gartner / Stanford / WAIC scored this run

## Independent context (not this run)

- [IMF data](https://www.imf.org/en/Data) (retrieved 2026-08-29)
- [BIS statistics](https://www.bis.org/statistics/index.htm) (retrieved 2026-08-29)
- [OECD data](https://www.oecd.org/en/data.html) (retrieved 2026-08-29)
- [Eurostat database](https://ec.europa.eu/eurostat/web/main/data/database) (retrieved 2026-08-29)
- [Our World in Data](https://ourworldindata.org/) (retrieved 2026-08-29)
- [Stanford HAI AI Index](https://hai.stanford.edu/ai-index) (retrieved 2026-08-29)
- [Gartner Peer Insights — Analytics and BI Platforms](https://www.gartner.com/reviews/market/analytics-business-intelligence-platforms) (retrieved 2026-08-29)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) (retrieved 2026-08-29)
- First-party homepage recognition only: [2026 WAIC Future Tech OPC Excellence Award](https://infinisynapse.com/#recognition)