Best of Google Cloud Platform — 2025
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Hacker News·1y
One line of code that did cost $8,000
Screen Studio, a macOS screen recorder app, inadvertently generated 2 petabytes of network traffic on Google Cloud due to a bug in its auto-updater. The issue arose from a missing code line that failed to stop downloads after the update file was obtained, causing repeated downloads every 5 minutes. This led to significant financial costs and user consequences, highlighting the importance of setting cloud alerts and carefully managing auto-updater codes.
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Javarevisited·1y
10 Best Cloud Computing Courses, Labs and Projects in 2025
Cloud computing is essential for modern software development and business operations in 2025. This guide provides a list of the 10 best interactive online courses, labs, and projects from Educative.io to learn cloud computing from scratch. These resources cover AWS, Azure, and Google Cloud Platform, providing practical skills for building and managing cloud solutions. It's an ideal resource for beginners to advanced professionals aiming to gain expertise and prepare for certifications.
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Awesome·49w
Redis just blew it and the alternative is way better...
Following a year of licensing controversies, Radius 8 has returned to open source. However, Valky, a fork backed by Amazon and others, has already gained traction, offering features Radius lacked. Radius's return to open source includes conditions that challenge cloud providers, unlike Valky's fully permissive model. The transition marks shifting dynamics in the open source and cloud landscape.
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Google Developers·39w
The Google Developer Program is evolving
Google Developer Program introduces new monthly subscription tier at $24.99/month, launches centralized forums at discuss.google.dev, and expands AI tool access. The program now offers flexible pricing, unified community platform with migrated existing forums, and enhanced benefits including Google Cloud credits, Gemini Code Assist Standard, and Firebase Studio Workspaces. A new Google Cloud & NVIDIA Community provides specialized learning pathways for AI inference tasks.
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Medium·1y
Building Multi-Agent with Google's A2A (Agent2Agent) Protocol, Agent Development Kit(ADK), and MCP (Model Context Protocol) - A Deep Dive(Full Code)
Explore building a multi-agent AI application using Google’s A2A protocol, ADK, and MCP. Delve into core concepts like task-based communication, agent discovery, framework-agnostic interoperability, multi-modal messaging, and standardized message structures. Learn about a practical demo involving a multi-agent travel planner that showcases these principles in action.
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Firebase Developers·26w
Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI
Arc is a privacy-focused messaging app built with Flutter for cross-platform development, Firebase for real-time messaging and authentication, Cloud Run for scalable microservices, and Vertex AI with Gemini models for AI-powered conversational companions. The architecture emphasizes four pillars: stability, speed, lightweight design, and high security. Key features include ephemeral messaging, mesh networking for offline resilience, and RAG-based AI characters grounded in Vertex AI Search to prevent hallucinations. The team achieves rapid iteration with 144 updates in 34 months through rigorous Flutter optimization using static analysis and dynamic profiling.
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Google Cloud·33w
Now available: Rust SDK for Google Cloud
Google Cloud has released an official Rust SDK providing access to over 140 Google Cloud APIs. The SDK includes built-in authentication support, comprehensive documentation, and covers services like Vertex AI, Cloud Key Management, and Identity Access Management. Previously, developers had to rely on unofficial SDKs with limited features and security concerns. The new SDK is available on crates.io and GitHub, offering a supported and idiomatic way to integrate Rust applications with Google Cloud services.
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Tech Lead Digest·45w
When Google Sneezes, the Whole World Catches a Cold
A detailed analysis of a major Google Cloud IAM outage that cascaded across multiple services including Cloudflare and Anthropic. The incident began with an IAM backend rollout issue at 10:50 AM PT, causing authentication failures across GCP products. Cloudflare's Workers KV, which depends on Google Cloud storage, failed next, affecting Access, WARP, and Zero Trust features. Anthropic disabled file uploads to manage error rates. Full recovery took over 7 hours, with some specialized services like Vertex AI requiring additional time. The analysis includes a detailed timeline, impact assessment, and lessons learned about dependency chains, control plane failures, and recovery patterns in distributed systems.
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Addy Osmani·20w
AddyOsmani.com
Addy Osmani announces his transition from Chrome developer experience to a director role at Google Cloud AI. After nearly 14 years with Chrome, he's now focusing on helping developers and businesses succeed with Gemini, Vertex AI, and the Agent Development Kit. His role bridges Google DeepMind, engineering, product, and developer relations teams to improve enterprise AI adoption and developer experience.
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Vertex AI RAG Engine: A developers tool
Generative AI and LLMs face issues like hallucinations and limited knowledge. RAG and grounding help by accessing up-to-date data for more accurate responses. Vertex AI RAG Engine, a managed service by Google Cloud, simplifies this process for developers, enhancing applications in various fields such as finance, healthcare, and legal, by streamlining data retrieval and improving accuracy.
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freeCodeCamp·1y
A Beginner's Guide to Terraform – Infrastructure-as-Code in Practice
Cloud development has evolved, making Infrastructure-as-Code (IaC) more prominent. Terraform, a tool from HashiCorp, stands out for its cloud-agnostic nature and ease of automation. This guide explains Terraform's benefits, like its declarative approach, conflict handling, and version control. It also details common Terraform commands and illustrates creating a Google Cloud-based virtual machine using Terraform scripting.
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Fireship·45wThat time Google Cloud Platform bricked the Internet…
Google Cloud Platform experienced a major outage that affected popular services like Snapchat, Spotify, Discord, and Cloudflare, causing nearly 100% error rates for over 2 hours. The incident was caused by a dormant bug in Google's API management service - a null pointer exception in code that lacked proper error handling. The bug was introduced on May 29th but remained undetected until a policy change on June 12th triggered the faulty code path, causing the API management binary to crash globally. Google took 40 minutes to begin rollback and 4 hours to fully stabilize, potentially costing millions in SLA credits and damaging their reputation in the competitive cloud market.
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So You Want to Build Your Own Data Center
Railway decided to build its own data center to overcome the limitations posed by hyperscalers like Google Cloud Platform, which affected pricing, service levels, and engineering constraints. After nine months, their first site in California was up and running, and additional regions are being set up. The post covers the entire process, from choosing the space, managing power and cooling, and ensuring optimal network connectivity, to the installation of hardware and configuration. It emphasizes the importance of redundancy, professional cabling, and detailed documentation for a successful data center build.
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Event Store·1y
Kurrent Cloud: Introducing Shared Infrastructure
Kurrent Cloud has introduced a new Shared Infrastructure feature, focusing on scalable, secure, and efficient deployments using Kubernetes. This marks a significant evolution in the company's cloud-native architecture, offering multiple deployment options including fully hosted, bring your own cloud (BYOC), and hybrid models. The platform architecture includes separate control and data planes, utilizing Kubernetes for elasticity, cost efficiency, and wide community support. Shared Infrastructure provides an entry point for users, with different deployment tiers for development and production environments.
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How I built an agent with Pydantic AI and Google Gemini
Building an AI agent leveraging Pydantic AI and Google Gemini can streamline the synthesis of strategic insights. This tutorial demonstrates how to build such an agent using frameworks like FastAPI for the backend, HTMX for dynamic UI updates, Tailwind CSS for styling, and Cloud Run for deployment. The agent performs web content analysis and generates a SWOT analysis, incorporating community insights and competitive analysis through various integrated tools.
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Awesome Go·1y
Parallel Streaming Pattern in Go: How to Scan Large S3 or GCS Buckets Significantly Faster
Parallelizing file operations in large GCS or S3 buckets can significantly improve performance. By using the rill concurrency toolkit in Go, tasks like listing, filtering, and deleting files can be made concurrent. This involves creating split points within the bucket to distribute the workload more evenly and efficiently using goroutines. The strategy minimizes bottlenecks and maintains cost efficiency. The provided examples demonstrate implementation for both GCS and S3.
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Google Open Source Blog·31w
Apache Iceberg 1.10: Maturing the V3 spec, the REST API and Google contributions
Apache Iceberg 1.10.0 introduces major improvements including full Spark 4.0 and Flink 2.0 compatibility, production-ready Deletion Vectors for faster row-level updates, and a hardened REST Catalog API. The release matures the V3 specification with features like row lineage and variant types. Google contributed native BigQuery Metastore Catalog support and Google AuthManager, enabling seamless integration with BigLake-managed tables through open REST protocols.
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Data Engineer Things·1y
Netflix Movie Analytics (Homemade)
A data engineer combines a passion for film with data analytics by analyzing their Netflix viewing habits. Using data exported from Netflix and enriched through The Movie Database (TMDB) API, they store and process the data on Google Cloud Platform (GCP). The data is modeled into a Star Schema on Google BigQuery, orchestrated with Airflow, and visualized using Tableau. Key insights include favorite genres, preferred viewing days, and overall streaming patterns.
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Theo - t3․gg·22wNVIDIA's first real competition (Google is KILLING it)
Google announced its seventh-generation TPU (Ironwood), claiming 10x performance improvements over previous versions and positioning itself as a serious competitor to Nvidia in AI accelerator hardware. Meta is reportedly in talks with Google for a multi-billion dollar chip deal starting in 2027, causing Nvidia's stock to drop 4% and wiping $112 billion off its market cap. Google is the only major tech company operating across all AI layers: applications (Google Search), foundation models (Gemini), cloud inference (GCP), and custom accelerator hardware (TPUs). The move represents a strategic shift as companies seek alternatives to Nvidia's dominant position and pricing in the GPU market, with Google leveraging its vertical integration and custom silicon expertise to challenge the status quo.
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Groww Engineering·38w
Migrating to Self-Hosted CodePush: Our Journey to Independence
Groww Engineering successfully migrated from Microsoft's retiring CodePush service to a self-hosted solution built on Google Cloud Platform. The new system uses GCP Cloud Storage buckets for data and bundles, CloudFlare for edge caching, and maintains API compatibility with minimal React Native app changes. Key achievements include 90% of users downloading updates in under 30 seconds and improved performance through edge caching. The migration involved modernizing legacy code from Promise polyfills to native JavaScript Promises and implementing custom storage adapters for GCP integration.
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TechCrunch·31w
It isn’t your imagination; Google Cloud is flooding the zone
Google Cloud is pursuing a strategy to capture emerging AI startups while industry giants like Nvidia, OpenAI, Microsoft, and Amazon form massive exclusive partnerships worth hundreds of billions. Google's COO Francis deSouza reveals that 60% of generative AI startups use Google Cloud, with the company offering $350,000 in credits, technical support, and open infrastructure access. This approach contrasts with competitors' mega-deals, positioning Google to benefit from the next wave of AI unicorns before they become too expensive to court.
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Tools Make an Agent: From Zero to Assistant with ADK
Google's Agent Development Kit (ADK) enables building AI agents that can interact with external systems through various tool types. The framework supports function tools for inline calculations, built-in tools like Google Search, third-party API integrations via LangChain, and Model Context Protocol (MCP) tools for databases and APIs. A practical example demonstrates creating a bug assistant agent for a coffee company that can search tickets, query GitHub issues, and interact with PostgreSQL databases using these different tool types.
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Advancing agentic AI development with Firebase Studio
Firebase Studio introduces three new Agent modes for AI-assisted development: Ask mode for conversational planning, Agent mode for supervised code changes, and Agent Auto-run mode for autonomous development tasks. The update includes foundational Model Context Protocol (MCP) support for workspace customization and direct integration with Gemini CLI for terminal-based AI assistance. These features leverage Gemini 2.5's reasoning capabilities and support project-level rule files for personalized guidance.
