PUBLIC EXPERIMENT LOG

AI Experiment Log

Public records of Experiments in which AI contributed to planning, execution, analysis, or documentation—structured for later verification and reuse by people and AI systems.

About AI Experiment Log

AI Experiment Log is a public experimental knowledge project that records investigations in which AI contributes to research, planning, coding, analysis, or documentation under human governance.

The records are designed to preserve not only conclusions, but also observations, evidence states, unsuccessful approaches, provenance, and—when safe and available—raw data or source code. Important claims distinguish what was observed, verified, inferred, or remains unverified.

The project covers practical experiments including home energy, IoT devices, environmental monitoring, APIs, automation, data acquisition, and protocol investigation. English and Japanese pages are maintained as corresponding public representations of the same experimental knowledge.

Recent Experiments

AIEL-2026-0004 · COMPLETED

SwitchBot CO2 alerts to a family LINE group with Google Apps Script

A roughly five-minute SwitchBot CO2 collection path was extended with persistence, hysteresis, and LINE group push notifications. Runtime tests verified the initial warning, one repeated warning exactly 30 minutes after the initial warning, the silent-reset effect after at least 10 minutes below 900 ppm, and a new INITIAL alert after reset.

Read record →

Topics

Published Experiments currently cover the following technical topics.

Google Apps Script (4)  SwitchBot (4)  Home IoT (3)  IoT (3)  CO2 Monitoring (2)  Energy Monitoring (2)  API (1)  API Authentication (1)  Cloudflare Workers (1)  Data Collection (1)  Data Quality (1)  ECHONET Lite (1) 

How to use these records

Each Experiment separates observations, verified facts, inferences, unresolved items, and failures where possible. When safely publishable, records also provide references to raw data, source code, or machine-readable experiment data.

Read the record & publication methodology →

All Experiment Records

AIEL-2026-0005 · COMPLETED

Rinnai API 401 ERR_0010: recovering a Kantakun Apps Script collector

A Google Apps Script Kantakun collector received Rinnai HTTP 401 / ERR_0010. The failure was localized to the Rinnai authentication path, and the retained implementation did not demonstrate a verified automatic credential-renewal path. A later retained record states that authentication was restored, but the exact recovery operation is unknown.

Read record →

AIEL-2026-0004 · COMPLETED

SwitchBot CO2 alerts to a family LINE group with Google Apps Script

A roughly five-minute SwitchBot CO2 collection path was extended with persistence, hysteresis, and LINE group push notifications. Runtime tests verified the initial warning, one repeated warning exactly 30 minutes after the initial warning, the silent-reset effect after at least 10 minutes below 900 ppm, and a new INITIAL alert after reset.

Read record →

AIEL-2026-0002 · COMPLETED

Open-Meteo HTTP 429 only on Apps Script time-driven triggers

The same Google Apps Script collector returned HTTP 200 when run manually and HTTP 429 under a time-driven trigger. The evidence points more specifically to the Google-side outward path used by scheduled execution than to collector request logic. Aggregation with other Apps Script traffic through a shared Google egress identity remains a hypothesis, while a Cloudflare Worker relay restored the investigated workflow.

Read record →

Legacy experiment records (pre-AIEL migration)

SWITCHBOT-HOME-ENV-001-R1 · completed

Long-term home environmental sensing with SwitchBot, Google Apps Script, and external weather observations — revised

A residential environmental logger continuously collects 12 SwitchBot sensor endpoints at a nominal five-minute cadence. The original physical analysis used 104,578 rows from 2026-07-28 through 2026-08-13. A 2026-09-06 longitudinal audit found 270,760 private Measurements rows: 270,698 SUCCESS and 62 ERROR, a row-level API-state success fraction of about 99.977%. Raw row count is not equivalent to the number of unique nominal five-minute observation slots, so completeness requires timestamp normalization.

Read record →

Amazonのアソシエイトとして、AI Experiment Logは適格販売により収入を得ています。
As an Amazon Associate, AI Experiment Log earns from qualifying purchases.