Gists

When we use generative AI to proofread our daily writing—whether technical reports, business proposals, or blog posts—it is easy to fall into the habit of simply copying and pasting AI-rewritten text without reflecting on what was changed or why. While that fixes the immediate draft, it rarely helps us grow as writers.

Recently, Google introduced powerful new capabilities to programmatically manage comments and suggestions across Google Docs, Sheets, and Slides via the Google Workspace APIs (see the official guide: Work with suggestions and comments in the Google Docs API).

Gists

📄 Published Paper (Open Access on Zenodo):
Autonomous Discovery of Unified Multi-Hazard Prediction Theory from Netatmo IoT Weather Big Data Using AlphaEvolve
🔗 Full Paper URL: https://zenodo.org/records/23354598
💻 Sample Scripts Repository (GitHub): https://github.com/tanaikech/alphaevolve-multi-hazard

“The book of nature is written in the language of mathematics.” — Galileo Galilei
Today, that mathematical language can be autonomously written as an algorithm, assembled into a predictive model, and elevated into a physical theory by artificial intelligence.

Gists

Deterministic Sub-Second Python and Bash Execution, Zero-Trust gVisor Isolation, and Zero Idle Cost

Abstract

While secure sandboxes are pivotal for running Generative AI-generated code safely, connecting Google Cloud Run Sandboxes (gVisor) directly to Google Apps Script unlocks a vastly broader horizon. Beyond executing AI-drafted scripts on the fly, this complementary architecture empowers Google Workspace with deterministic Python data science (Pandas, Seaborn) and Bash execution in 200–450 ms. With zero-trust micro-isolation, zero-token data ingestion, and zero idle cost, it elevates Workspace automations far beyond standard V8 runtime constraints.

Gists

Turn Google Sheets into a Fully Interactive, Differential ACID Mutation Console for Apache Iceberg without Reverse ETL SaaS or Cloud Servers.

Hero Infographic: Interactive Bidirectional Lakehouse Writeback via Google Sheets & Apache Iceberg. Enables business operators to query filtered records from an open Apache Iceberg table on Google Cloud Storage, visually edit values, add new rows, or purge obsolete records directly within a Google Sheets grid with an embedded dark-themed console, and commit atomic, microsecond-tolerant ACID mutations back to Parquet storage via BigQuery without Reverse ETL SaaS or persistent servers.

Gists

Turn Google Drive into an AI-Powered Lakehouse Vector Engine across Converted PDFs, Binary Images, and Text without Specialized Vector Databases.

Hero Infographic: Unified Multimodal Lakehouse Vector Search via Apache Iceberg & Google Apps Script. Consolidates Google Docs, Sheets, Slides, Forms, binary diagrams, and web-fetched assets into an open Parquet table, driven by Gemini embeddings and BigQuery serverless pushdown for sub-second semantic retrieval and live Google Drive discovery.

Gists

Turn Google Sheets into a Petabyte Lakehouse with Sub-Second ACID Queries.

Abstract

Google Sheets faces severe scalability bottlenecks when handling large enterprise datasets, including a strict 10-million cell limit, crippling CRUD latency, and Google Apps Script’s execution and memory boundaries. This article presents IcebergApp, an open-source library bridging Google Workspace to Apache Iceberg lakehouses on Google Cloud. By utilizing BigQuery as a serverless query accelerator to execute predicate pushdown over Iceberg metadata, IcebergApp eliminates spreadsheet latency, ensures ACID transactions, and enables sub-second queries, time travel, and agentic AI integration directly from Google Apps Script.

Gists

Published Paper: Capturing Volcanic Lamb Waves and Blast Yields with Netatmo Weather Networks (ESS Open Archive)

Abstract

Explosive volcanic eruptions generate powerful atmospheric Lamb waves—acoustic-gravity waves trapped in the planetary boundary layer that travel horizontally across the globe at sound speed. Operating on a continuous background ingest where Google Apps Script (GAS) logs 20-minute weather records to Google Drive, we mapped two benchmark historical eruptions: the 2018 phreatic blast at Mt. Kusatsu-Shirane and the 2018 magmatic explosion at Mt. Shinmoedake. We framed these events within first-principles continuum mechanics inside the Antigravity CLI environment powered by Gemini and the autonomous R&D framework tanaike-lab. Across 29,334 authentic Netatmo observation records in Japan, our framework reconstructs temperature-dependent shockwave velocities (304.38 m/s in sub-zero winter air and 313.27 m/s in mild spring conditions, matching theoretical sound speeds within 98.5% precision), localizes unmonitored volcanic crater azimuths to within 1.78 degrees, quantifies explosive yields (178.8 to 1,041.1 tons of TNT equivalent), and delivers 2.5 to 15 minutes of advance early warning with 100% false alarm elimination (0.0% False Positive Rate) during violent storm baselines. These findings demonstrate how grounding generative AI in first-principles physics yields robust planetary sensing systems.

Gists

Breaking the Limits of GAS with Direct Cloud-to-Cloud Streaming in Persistent Linux Sandboxes

Abstract

While Google Apps Script (GAS) is a powerful tool for Google Workspace automation, platform and computational constraints often limit its ability to handle advanced workloads. Gemini Managed Agents provide remote Linux sandboxes equipped with bash execution. This article introduces an architecture integrating GAS with a Linux sandbox to execute tasks beyond the capabilities of Apps Script alone. By streaming generated artifacts directly from within the Linux sandbox to Google Drive, this approach bypasses API payload limits, eliminates token overhead, and achieves high-throughput cloud automation.

Gists

Abstract

Generative AI and autonomous agents do not obsolete Google Apps Script (GAS); they elevate it into an indispensable deterministic execution substrate. This article establishes an enterprise hybrid architecture dividing responsibilities between AI’s probabilistic reasoning (the brain) and GAS’s secure, zero-cost, event-driven execution (the nervous system). Through 12 production use cases—spanning MCP servers, deterministic guardrails, and hybrid batching—we formalize four foundational principles for engineering resilient, scalable Google Workspace automations.

Gists

Solving the LLM quota monitoring paradox with zero-overhead local Connect RPC agent hooks.

Abstract

Google Antigravity CLI users using Google OAuth face abrupt task failures when API quota hits 0%, while account switching triggers unrecoverable signature errors. Querying quota via LLM tool calls creates a paradox by consuming the very tokens being monitored. We resolve this with antigravity-cli-check-usage-plugin, a CLI Agent Hook running outside the LLM execution turn. Directly querying local Connect RPC endpoints, it monitors quota with zero token overhead and injects proactive warning banners when threshold limits are reached.