Google Cloud Tech
Every summary, chronological. Filter by category, tag, or source from the rail.
Rapid Prototyping and Deployment with Google AI Studio
Use Google AI Studio's build mode to generate, iterate, and deploy full-stack web applications via natural language prompts, bypassing manual coding for initial scaffolding.
Google Cloud TechBuilding and Deploying Full-Stack AI Apps with Firebase
Learn to build, secure, and deploy a real-time, full-stack to-do application using Google AI Studio and Firebase, leveraging automated authentication and real-time database synchronization.
Building and Deploying Turn-Based Web Games with AI
Learn to build real-time, turn-based web games using event sourcing, Firestore for state synchronization, and Google AI Studio for iterative debugging and deployment.
7 Modular Design Patterns for AI Coding Agents
Improve AI coding agent performance by replacing long, confusing prompts with modular 'skills'—specialized text files that the agent loads dynamically only when needed.
Google Cloud TechBuilding Real-Time Voice AI Agents with Gemini Live
Gemini Live enables bidirectional, audio-native conversations by using WebSockets for streaming and built-in voice activity detection to handle interruptions and tool execution.
Google Cloud TechBuilding and Scaling Multi-Agent AI Systems on GKE
A practical guide to deploying AI agents on GKE, using the Model Context Protocol for infrastructure troubleshooting, and implementing secure sandboxing for AI-generated code.
Strategies for Serving JAX Models in Production
Moving JAX models from notebooks to production requires choosing the right serialization and compilation strategy to avoid latency spikes caused by just-in-time compilation.
Scaling JAX Models to Multi-GPU Systems
Scale JAX models across multiple GPUs by defining array layouts with Mesh and PartitionSpec, allowing the compiler to handle gradient synchronization automatically.
Building and Optimizing JAX Training Loops
Build high-performance JAX training loops by maintaining pure functions, keeping data on-device, and utilizing fused kernels like cuDNN attention to avoid GPU memory bottlenecks.
Optimizing JAX Performance on NVIDIA GPUs
JAX performance hinges on ensuring your code runs on the GPU, maintaining stable input shapes to prevent re-compilation, and correctly handling asynchronous execution during profiling.
4 Common Loop Engineering Failures and How to Fix Them
Loop engineering automates repetitive tasks by setting goals and retrying, but it often fails due to runaway costs, confirmation bias, vague objectives, or excessive complexity. Success requires strict stop rules, external evaluation, concrete metrics, and transitioning to graph-based architectures for complex workflows.
Google Cloud TechModernizing Legacy Codebases with AI Agents
Tackle legacy code by treating AI as a coworker: use a three-step 'plan, execute, verify' workflow, prioritize test-driven development, and enforce strict guardrails to prevent hallucinations and errors.
Google Cloud TechBuilding Bidirectional Multimodal AI Agents
Moving from turn-based chatbots to 'omni-apps' requires a continuous loop of perception, reasoning, and expression that handles real-time voice, vision, and browser interaction.
Google Cloud TechBuilding AI Agents with Gemini Enterprise & Google Workspace
Learn how to integrate Gemini Enterprise agents with Google Workspace data and actions using connectors, MCPs, and no-code/pro-code development frameworks to automate enterprise workflows.
Google Cloud TechBuilding Resilient Notification Systems with Temporal & Cloud Run
Imaxxing, a viral movie ticket monitoring app, uses Temporal's durable execution and Cloud Run's serverless scaling to handle spiky traffic and unreliable downstream data sources without losing state.
Democratizing Startup Funding with AI Agents
Happly.ai uses AI vectorization and Gemini to help founders secure non-dilutive funding—grants, tax credits, and procurements—leveling the playing field for underrepresented entrepreneurs.
Google Cloud TechMaking Websites Agent-Ready with WebMCP
WebMCP allows developers to expose typed, contextual tools directly within web pages, enabling AI agents to interact with sites reliably and efficiently without relying on expensive, error-prone screenshot scraping.
Querying and Acting on Cloud Data with Data Agent Kit
The Data Agent Kit provides a unified framework of MCP servers, agent skills, and IDE integrations that allow AI agents to securely query, analyze, and modify data across BigQuery, Cloud SQL, and Cloud Storage.
Google Cloud TechArchitecting Secure, Serverless AI Apps on Google Cloud
Build scalable AI-powered mobile apps by combining Flutter for the frontend, Firebase for managed services, and Google Cloud for backend heavy lifting, while prioritizing security through model-level protections.
Google Cloud TechGoogle 'All Things Agentic' Hackathon Overview
Google is hosting a global hackathon with $180,000 in prizes, challenging developers to build autonomous, production-ready AI agents using Gemini 3.5 and Google Cloud.
The Hidden Costs of Token Maxxing
Token maxxing—the practice of using as many tokens as possible under the assumption that more is better—is an inefficient habit driven by a lack of exposure to the true economic costs of AI inference.
Google Cloud TechSecure AI Coding: A Framework for Production-Ready Agents
To use AI agents securely, treat them like junior developers: enforce small, test-driven batches, provide scoped context, use hardened sandboxing, and verify output with traditional security tooling.
From Tokenmaxxing to Tokenomics: Scaling AI Agents Sustainably
As AI usage shifts from experimental 'tokenmaxxing' to production-scale agentic loops, enterprises face a 'token panic.' The solution is Tokenomics: a new discipline focused on aligning energy consumption, model efficiency, and business value.
Google Cloud TechOptimizing AI Agents: MCP vs. Skills
While Model Context Protocol (MCP) standardizes how LLMs connect to external data, it suffers from context bloat. 'Skills' solve this by using progressive disclosure to load instructions only when needed, allowing for more efficient, modular agent development.
Google Cloud TechShifting from Writing Code to Reviewing AI Output
AI coding agents don't replace developer craft; they shift the primary responsibility from writing code to rigorous review, verification, and production safety.
Google Cloud TechOptimizing AI-Driven Development with Claude Code
Leverage Claude Code on Google Cloud for intent-driven development by using voice interaction, iterative prompting, and CLI-based automation to build and verify complex applications.
Google Cloud TechBuilding Complex Apps with Claude Code and Dynamic Workflows
Claude Code's new dynamic workflows allow developers to automate complex, multi-step coding tasks by generating deterministic, parallelized JavaScript execution plans that can be saved, edited, and reused.
The Reality of Vibe Coding and Developer Identity
Vibe coding—using AI to build without deep knowledge of underlying syntax—is shifting developer identity from 'code author' to 'code reviewer' and 'agent orchestrator,' raising questions about the future of junior roles and technical skill retention.
Google Cloud TechBuilding AI Data Agents with ADK and MCP
By using the Agent Development Kit (ADK) and Model Context Protocol (MCP), developers can build AI agents that query BigQuery in natural language, eliminating the need for custom SQL glue code and static dashboards.
Google Cloud TechAI Builder Essentials: Tokens, RAG, and Context Windows
LLMs operate on tokens—not words—and are inherently non-deterministic. To overcome training data cutoffs, use Retrieval-Augmented Generation (RAG) to inject real-time data, while managing context window limits and token costs to avoid inefficient 'token maxxing'.
Showing 30 of 141