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.

Gists

Motivation

To be quite honest, “Hooks”—the shell commands we trigger at specific points when generative AI agents process tasks—were something I used blindly for a long time. Whenever colleagues asked me about them, I realized I lacked any real confidence in explaining how they actually work. However, when I migrated from Gemini CLI to the new Antigravity CLI, I noticed that the hooks system carried over. This felt like the right moment to stop guessing and finally develop a precise, deep understanding of the mechanism. I went back to the basics to analyze exactly how hooks operate under the hood and how we can use them effectively in the Antigravity environment. My goal is to demystify hooks so we can write them with confidence, and if this guide proves useful to your own workflows as well, I would be very glad.

ggsrun was updated to v5.3.7.

• v5.3.7 (June 2026) - Simplified Quick Onboarding, On-demand Setup Prompting, Optional Credentials Path, and Seamless Configuration Initializer Introduced the groundbreaking, extremely easy-to-use $ ggsrun setup onboarding command to dramatically simplify Google Cloud API and OAuth2 credentials setup, while keeping traditional $ ggsrun auth fully intact for backward compatibility. This command automates Google Cloud Workspace API enablement (Drive, Sheets, Slides, Docs, Google Apps Script, Gmail) using GCP Quick Flows, immediately redirecting users straight to the Credentials creation page. It removes any credential filename renaming constraints (you can load credentials from any custom file path like {your path}/{credential name}.json rather than renaming to exactly client_secret.json) or supports manual credential copy-pasting. Added a dynamic config initializer (ggsrunIniForSetup) to gracefully bypass “missing client_secret.json” startup errors on first-time runs, and added interactive prompts to configure Script IDs and Web Apps URLs with automated pre-filled defaults from existing configurations.

You can check ggsrun at https://github.com/tanaikech/ggsrun.

GASADK was updated to v2.0.0

• v2.0.0 (June 24, 2026)
  • Exposed Google Workspace APIs as a Dynamic MCP/A2A Server.
  • Added a standalone deployment script (DeployMcpServer.js) under samples/googleapiapp-mcp-server to expose the GoogleApiApp library.
  • Created a detailed beginner-friendly guide for configuring and deploying Google Workspace services (Sheets, Drive, Docs, Calendar, Gmail, etc.) as dynamic MCP tools.
  • Added API Quota Safeguard: Track model token consumption per session and enforce session-level token usage limit check via maxTokensPerSession configuration.
  • Added Hook & Session Propagation: Automatically propagate parent hooks security context (hookManager and sessionId) and token constraints down to child sub-agents during hierarchical delegation.
  • Added Human-in-the-Loop (HITL) Integration: Supported decision: "suspend" decisions in BeforeTool hooks. Seamlessly serializes and stores task state to PropertiesService (under HITL_STATE_[sessionId]) and throws a "SUSPENDED" exception. Added saveState(), loadState(sessionId), and resume(sessionId, decision, approvedArgs) methods to recover and run suspended agent execution loops.
  • Synchronized src/lib/ Core Libraries: Upgraded bundled core components (GeminiWithFiles.js, A2AApp.js, MCPApp.js, MCPA2Aserver.js, and FileSearchApp.js) to support the GasHookManager hooks system natively (allowing interceptors like BeforeModel, AfterModel, BeforeTool, and AfterTool).
  • Ecosystem Modernization: Upgraded default model configurations to models/gemini-3.1-flash-lite across all workspace libraries, tests, and examples to provide modern, cost-effective defaults.
  • Modular Standalone Twin Synchronization: Propagated and fully published these updates to all five standalone local repositories (GeminiWithFiles_GAS_lib, A2AApp_GAS_lib, MCPApp_GAS_lib, MCPA2Aserver_GAS_lib, FileSearchApp_GAS_lib) with next-version tags.

You can see the detail information here https://github.com/tanaikech/adk-gas