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.

Gists

Published Paper: Unification Theory of Lithosphere-Atmosphere-Ionosphere Coupling via Acoustic-Gravity Waves (LAIC-AGW) and Quantitative Pre- and Post-Seismic Anomaly Verification Using Ultra-Dense IoT Weather Sensor Networks (ESS Open Archive)

Abstract

We announce the publication of our updated manuscript on ESS Open Archive, establishing the Unified LAIC-AGW Theory using ultra-dense IoT weather data. Executed on Antigravity CLI with Gemini 3.6 Flash using the autonomous R&D framework tanaike-lab, this project integrates 71,107 authentic Netatmo observation records with seismic moment tensors ($M_{ij}$). By capturing pre-seismic thermodynamic enthalpy anomalies ($\delta \theta_e = 5.96\text{--}15.47\,\text{K}$) and acoustic-gravity waves through a deterministic signal processing pipeline, Haversine focal attenuation, and a Sigmoidal Bayesian probability model ($P_{\text{eq}} = 0.0\%\text{--}99.8\%$), this framework delivers 2 to 6 hours of advance lead time with 100% false alert elimination in non-epicentral regions. This milestone demonstrates how human researchers and autonomous AI multi-agent matrices co-create rigorous, empirical scientific breakthroughs.

Gists

Abstract

Gemini Spark, Google’s 24/7 autonomous AI agent, seamlessly connects with Google Workspace. However, connecting to arbitrary external APIs requires extended integration. This article demonstrates how integrating Google Apps Script (GAS) as a Model Context Protocol (MCP) server or Webhook endpoint expands Gemini Spark’s capabilities, enabling enterprise-grade workflow automation.

Introduction

Gemini Spark was officially announced at Google I/O 2026 on May 19, 2026, and its service in Japan began on July 16, 2026. Ref Gemini Spark is Google’s first always-on AI agent that runs continuously in the cloud 24/7, even when your PC is closed, to complete tasks on your behalf. Unlike traditional Q&A-style chat AIs, it can autonomously execute complex workflows across multiple Google applications. Its core functionalities are built around three main pillars: Tasks, Skills, and Schedules.

Gists

System Enforces Order, AI Accelerates Logic: Driving Next-Generation R&D Through Human-AI Co-Creation

Abstract

This case study presents the zero-to-one execution of an urban torrential rain fluid dynamics research project using Gemini and tanaike-lab on Antigravity CLI. By uniting system order, AI-accelerated logic, and clear human imagination, we demonstrate a next-generation R&D paradigm that draws new scientific realities out of the dark void.

Introduction

The modern scientific research engine faces a nuanced challenge. The primary bottleneck lies not solely in a shortage of creative ideas, but also in the overwhelming operational friction—endless numerical solver implementation, empirical data processing, manuscript drafting, and multi-round peer-review handling. Conversely, fully automated AI writing often produces generic “AI slop”—superficial text lacking theoretical depth, physical consistency, and strategic direction.

Gists

Abstract

This article introduces Robust-GAP, a hierarchical Retrieval-Augmented Generation (RAG) framework designed to eliminate semantic hallucinations and knowledge drift during multi-document log summarization. By combining dynamic causal graph extraction (DLCE), active topology verification (SGAV), and metadata provenance propagation (PAPP), the framework enforces strict citation traceability and prevents LLM-generated hallucinations.

Note: The complete academic preprint detailing this research is openly available on Zenodo (DOI: 10.5281/zenodo.21436390).

1. Introduction

Standard Retrieval-Augmented Generation (RAG) pipelines fail when aggregating unstructured multi-document event streams because they lack causal tracking. When large language models (LLMs) summarize logs, chat transcripts, or transaction records, they frequently associate unrelated events. This behavior, known as knowledge drift, creates false dependencies.

Gists

Today, on July 16, 2026, the Antigravity CLI was updated to version v1.1.3. Following this update, I encountered a critical issue: after entering any prompt, the execution state falls into an infinite loop. The status messages (such as “Loading…”, “Generating…”, and “Working…”) cycle indefinitely, and no content is ever generated.

Is this issue isolated?

At this moment, it remains unclear whether this issue is isolated to my specific environment/account configuration or is a widespread bug affecting other users.

Gists

Have you ever had a brilliant solution get completely crushed by a single comment on a technical blog post?

Just last week, on July 8, 2026, I published a post introducing the Pyramid Aggregator. It was a beautiful story of technical lineage: we took a string summation algorithm originally designed on October 13, 2016 (to optimize Google Apps Script) and applied it to modern Large Language Models (LLMs). By organizing document merging into a balanced, parallel tree topology, we successfully bypassed the AI’s “Lost in the Middle” bias and achieved a 95% execution speedup using the Antigravity Python SDK.

Gists

Have you ever tried to read a massive pile of reports and summarize them in under 50 words? It’s hard. Now, imagine asking a cutting-edge Large Language Model (LLM)—like Gemini—to do it.

You might think AIs have perfect memories, but they don’t. When forced to aggregate information from dozens of documents under strict length constraints, AIs suffer from severe “memory loss” biases. They either ignore the middle of your documents or completely forget the older information they read first.

Gists

Abstract

Executing autonomous AI agent payloads in Google Workspace via the Apps Script API’s scripts.run method introduces severe security risks. This article presents a novel sandboxing proposal designed specifically for the scripts.run method, using ggsrun as the orchestrator to execute code safely and efficiently. By performing in-memory token replacement and uploading a separate, alphabetically-prioritized guard file, this approach achieves robust API-level containment. Guided by ggsrun’s automated backup and default rollback lifecycle (exe1), the remote environment is immediately restored, providing a clean, dependency-free security model for AI-driven Workspace automation.