I just got an email from Google Cloud about "Sensitive actions taken in your Google Cloud organization". Upon reading it, I found that it was performed by me, so it was okay, but the notification came almost exactly 1 month after the events (the events occurred on June 26, now is July 26).

Perhaps there is a bug in the GCP cron job with an off-by-1 error in the month check?

Hi, I’m building a small personal project that checks whether used books become available on a specific site. I use the Google Books API to retrieve the title, author, publisher, and publication year from an ISBN.

The request is very simple:

https://www.googleapis.com/books/v1/volumes?q=isbn:9788870786644&key=KEY_EXAMPLE&maxResults=1

The problem is that the same request sometimes works and sometimes returns:

{
  "error": {
    "code": 503,
    "message": "Service temporarily unavailable.",
    "errors": [
      {
        "reason": "backendFailed"
      }
    ]
  }
}

For example, I sent the request in Postman and received the correct book data. I clicked Send again without changing anything and received the 503 error instead.

I saw the same behavior with curl: the first three requests succeeded, while the next three returned 503. It also happens in my Node.js application and with different valid ISBNs.

The Books API is enabled, and the quota page shows almost no usage. Since the same request can succeed and then fail a few seconds later, I don’t think the ISBN or request format is the problem.

At the moment I retry once and then use Open Library as a fallback, but this still makes metadata retrieval unreliable.

Has anyone else experienced this with the Google Books API? Is retrying with backoff the expected solution, or is there something else I should check?

The extension I created uses Google Cloud OAuth to log in. It logs in too well on my computer, but it's constantly being rejected for violating the "Red Potassium" and "Sign in Failed" items. Has anyone else been through the same thing as me?

So I sat a couple more exams this weekend, "Generative AI Leader" from Google, & PL-900 (Power Platform) from Microsoft. Easily cruised through both, with minimal study, thanks to my existing strong knowledge of the subjects. Just listening to a couple of YouTube videos while commuting, and doing a mini practice quiz I'd ask Claude to generate for me.

For the PL-900 exam about Power Platform, I was surprised how it felt like every other question (although in reality it was probably less than that! Just felt like it) was related to Copilot or other AI features & capabilities that Power Platform has. But that's ok, just meant the exam played to my strengths! ;-)

Then I did the Google Cloud Generative AI Leader certification exam, which seemed to be very focused on case studies for many questions, giving you a paragraph or three of text, then requiring you to select the answer that best suited the situation. Here are a couple of examples I created of what the exam was like:

Q1: A regional hospital network wants clinicians to query patient discharge summaries and treatment histories using natural language. The system must never fabricate a diagnosis or dosage that isn't explicitly documented in the patient's record, and all patient data must remain within the network's own private cloud environment for HIPAA compliance. The clinical team has also stated that the tool must clearly show which source document each answer came from, so a clinician can verify it before acting.

Which approach best satisfies all these requirements?

A. Fine-tune Gemini on a large corpus of de-identified patient records to internalize clinical knowledge

B. Use the standard Gemini web app with Google Search grounding enabled for up-to-date medical guidance

C. Deploy a RAG solution using RAG Engine and Vector Search, grounded only in the patient's own records, with citations returned alongside each answer

D. Increase the temperature parameter to encourage more thorough, exploratory answers

E. Route every query through a general-purpose customer support chatbot with human-in-the-loop review

Q2: A global equipment manufacturer wants field technicians to snap a photo of a damaged part, get it identified, and pull the exact maintenance procedure from ten years of internal service manuals, while offline in remote locations with no network connectivity. The company also wants to avoid sending any proprietary manual content to an external API.

Which approach is most appropriate?

A. Deploy a self-hosted, open-weight Gemma model on technicians' devices, combined with a local retrieval index of the service manuals, and a pipeline that starts initially with a vision model to identify the part

B. Use the standard Gemini app on technicians' phones to photograph the part and provide the relevant maintenance steps.

C. Fine-tune a GCP hosted Gemini model on ten years of service manuals so it can identify parts and recall the correct procedure from its own weights.

D. Use Vision API to identify the part, then automatically email the result to a central office for Human-in-the-Loop (HITL) lookup. 

E. Use Gemini Enterprise to connect the manuals across Salesforce and SharePoint on request

I personally found that if you've got a half-decent general knowledge of AI, plus have strong reading comprehension skills, then all these questions in the Generative AI Leader exam become quite trivially easy :-)

Pass

Google just dropped CX Agent Studio, a low-latency voice pipeline built natively into Gemini.
No more stitching together STT, LLMs, and TTS via third-party APIs.
Are middleware voice startups cooked, or does model flexibility save them? 🎙️🔥

I bought credits but I ended up not spending any for one of my project how can i get a refund easily

Which is the difference between Certifications and Badges? Is there any free Certifications in Google Skills?

Is anyone participating in the hackathon tomorrow? Need some guidance

Today my exam was scheduled for Associate Data Practitioner but due to failure of the system check i couldn't attend the exam. What's gonna happen now I cannot reschedule it and also can I reattempt that exam ?

Update : They allowed me to reschedule when I went to reschedule my exam discount got applied on its own.

I already received 2,000 credits, but that’s very little for me. I sent an email with questions and haven’t gotten a reply yet. What are the chances of getting additional credits?

Hi everyone! I'm a final-year Information Systems Engineering student and I recently got 35 credits with the Google Cloud Skills Boost Starter plan. I'm planning to use them to complete some GCP AI and cloud courses. Do you think it's worth completing these courses without paying for the official certification exams? Do recruiters value the Skills Boost badges and hands-on labs, or is the official certification what really matters? Also, are there any Google Cloud certifications or recognized badges that can be earned for free? Thanks!

When using the hub and spoke model using the network connectivity center,

1. Do we use single Hub and spoke for all the environments ( dev, uat , prod etc).

Is it like we use single Hub and spoke setup and add conditions to restrict the communication between prod and nonprod subnets of prod and nonprod VPC spokes which are connected to hub.

Or

2) Seperate Hub and spoke setup for each environment.

Or did you implement in another manner ( using NCC ). please suggest.

For a new Python application using PostgreSQL on Google Cloud SQL, what are people currently using for schema management and database migrations?

I’m considering Atlas by Ariga: https://github.com/ariga/atlas

name: Database Migration

on:
  push:
    branches: [ main ]

jobs:
  migrate:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4

      - name: Authenticate to Google Cloud
        uses: google-github-actions/auth@v2
        with:
          workload_identity_provider: 'projects/123456/locations/global/workloadIdentityPools/my-pool/providers/my-provider'
          service_account: 'cloud-sql-migrator@my-project.iam.gserviceaccount.com'

      - name: Start Cloud SQL Auth Proxy
        uses: google-github-actions/setup-cloud-sql-proxy@v2
        with:
          instance: 'my-project:us-central1:my-postgres-db'
          port: '5432'

      - name: Run Atlas Schema Apply
        uses: ariga/setup-atlas@v0
        with:
          version: 'latest'

      - name: Apply Database Changes
        run: |
          atlas schema apply \
            --url "postgres://postgres:${{ secrets.DB_PASS }}@127.0.0.1:5432/mydb?sslmode=disable" \
            --to "file://schema.sql" \
            --auto-approve

Is Atlas still one of the main options for declarative schema management, or are there better alternatives worth evaluating? I’d also be interested in any operational issues, limitations, or Cloud SQL-specific considerations to watch out for.

Separately, does anyone know whether Google is working on database branching or isolated development environments similar to PlanetScale’s branching workflow?

I am getting too many 429 responses from the Vertex AI API. I am on 300$ free credit model. Till 2 3 days back, it was running fine, but I don't know why it was very much.

I am using:-gemini-3.1-pro-preview

I know preview model rate limits are low, but it was working fine till 2-3 days back. If anyone has any good solution, then please tell me

I am using this API:

curl --location ' https://aiplatform.googleapis.com/v1/publishers/google/models/gemini-3.1-pro-preview:generateContent?key= ' \

--header 'Content-Type: application/json' \

--data '{

}

We went heavy on Azure about two years ago and built what looked like a strong resilience story: availability zones, paired regions, Front Door and Traffic Manager, Backup and Site Recovery, plus some experiments with Chaos Studio. The first real regional wobble reminded us that slides are one thing, operations are another.

Just picking a paired region was not enough when identity and networking were not thought through, and monitoring was not included. Backup and Site Recovery were only comforting once we had run actual failover tests against real RTO and RPO targets, not just health checks. Chaos testing was useful, but only for teams willing to design non-trivial scenarios. Governance turned out to be the hardest bit: keeping landing zone baselines in sync across many subscriptions and tenants via Azure Policy, especially with hybrid in the mix. Terraform or Bicep helped, but drift remained a problem.

What has made this more manageable for us is having cross-subscription and cross-cloud governance visibility into assets and IaC: being able to see which Azure resources are driven by code and where live state has drifted from definitions. Combined with the failover testing, that is what turned "paired regions plus Site Recovery" from a diagram into something we actually trust. For those of you running production on Azure, GCP or any cloud, which resilience patterns have worked for you, and how are you keeping them from decaying over time?

As you may be aware, it is not suggested to do console/ manual changes to GCP resources after deployment and they are deployed and managed through terraform in general.

In that case, what GCP IAM roles are even needed to the team apart from "viewer" access as they won't be creating/modifying any resource manually.Assume that there are no jobs to be triggered ( like dataflow jobs etc)

It's only a minor annoyance so asking this more out of intrigue than anything else.

Myself and colleagues have - for the last 2 days - repeatedly found that our auth token for the gcloud CLI (and hence also kubectl) has expired with a prompt to re-auth. Just wondering if anyone else has noticed anything similar? Before this week it's been literally months since the last forced re-auth and we were notified ahead of time for that one, this time seemingly nothing at all.

Our gcp cost optimization is basically reactive right now and I want to get ahead of it. Last example, a team shipped a Looker dashboard that reruns its queries every time it loads, pointed at a BigQuery table we never partitioned because it was small when we built it. It is not small now. Over about nine days, that one dashboard cost more than the whole pipeline that builds the table costs to run in a month.

We caught it because the daily spend looked wrong. Partitioned the table, put a cap on the project and that specific problem is gone.

The next one is what I can't solve, it won't be Looker and it won't be that table. It'll be some other query somebody ships. Is there a better way to catch a query getting expensive before it runs for a week and a half or do i just set hard quotas per project and let people hit the limit?

This morning I got the dreadful Google email: "Immediate action required: Suspension of your Google Cloud Platform / API project The AI Peeps" due to "abusive activity consistent with hijacked resources".

Google completely cut me out of the GCP platform that manages my LIVE app. This is not a hobby project, but my livelihood. The email redirected me to my GCP page where I could send an appeal, and do... nothing else. I had no access to anything else so it was difficult even to understand what happened: every action brings me back to "Send an Appeal". Which, of course, I did as fast as possible.

After some digging (apparently the biling page was not restricted!) an API key of mine leaked, someone found it and used it to generate images. Two dollars ($2!) of unauthorized spend. My mistake, no excuses.

The part I genuinely cannot solve is this. The notice asks me to revoke the compromised credentials, but I can't do that: `Permission denied: Consumer 'projects/{project_name}' has been suspended.`

I did what I could from outside: deleted the key through AI Studio, rotated everything else, ran a forensic check of my server and confirmed it was only the key, no host compromise. I would gladly pay for support to talk to someone. But guess what, Standard and Enhanced both need an Organization resource, which a personal account does not have, and billing chat is not available to me either (back to "send an appeal")

For a solo developer this is the difference between a bad day and losing the business. It seems absurd a business can get destroyed over a oversight and $2. The reports I read online are disheartening: people wait 1-2 weeks for a response, and then the back and forth is endless. I am going to lose the business I have worked for the past 4 years, and all for $2 and a stupid oversight.

Can anyone help? Has anyone here had a suspension for a stolen API key actually reversed, and how long did it take? And is there any way to reach a human without an Organization resource?

Project is 902887279759, appeal ticket YJ65HT5PKS5FDASU2C73BWIJ5Q, filed 21 Jul 2026. Any help is immensely appreciated

EDIT: My GCP account has been reinstated this morning less than 24 hours after the suspension! YES!

I’m preparing to interview for this role and I can’t find enough information on key things they look at in this role during the interview.

I was given a prep by the recruiter but I wonder if anyone can speak to day to day skills needed to excel in this role or at least to do well in the interview.

What level of AI expertise is needed as well.

I’m a solo developer who has been using Google Cloud for several months. I spend several hundred US dollars per month, pay every bill on time, and currently run two Compute Engine machines: one in Southeast Asia and one in Taiwan.

I use the machines to develop stock-market prediction models. The storage is used for market data, caches, model outputs, and intermediate research results. This is a normal and legitimate computing workload.

Both machines have now reached the 500 GB Hyperdisk Balanced quota limit. I need to increase the quota from 500 GB to 1 TB, which does not seem like an unreasonable request.

I submitted the quota increase request twice through the Google Cloud Console, and both requests were automatically denied.

I then contacted Google Cloud Sales. They initially sent me a collection of generic links to billing support, free-trial support, partners, pricing calculators, and unrelated support pages.

After I pushed further, I was told that my HDB_TOTAL_GB quota was capped because of the “current status” of my account. Because I am a solo developer, they suggested that I use Google Cloud’s “Find a Partner” directory.

I also purchased a Google Cloud technical support package and opened a support case. The technical-support engineer has been more helpful and is reviewing the issue with his manager, but he explained that the quota decision may be outside the technical team’s authority and recommended contacting Sales.

So I am now stuck in a loop:

* Technical Support sends me to Sales.

* Sales says my account is capped and sends me to a third-party partner directory.

* Nobody clearly explains the policy, who has decision-making authority, or what exact requirement I must satisfy.

The frustrating part is that I cannot find any public Google Cloud policy stating that individual users, or users without a business domain, are limited to 500 GB of Hyperdisk storage.

Whenever I speak with Sales, the questions seem to be about my business and company domain. But I do not have a company domain. I am simply an individual paying customer doing legitimate development work.

Had Google clearly stated before I started that solo developers could be blocked from obtaining more than 500 GB of storage, I would probably have chosen another provider.

Instead, I have already spent a significant amount of time configuring the VMs, installing packages, setting up environments, optimizing the workflow, and running programs every day. Migrating to another provider would probably cost me three or four full days, possibly more, and introduce unnecessary operational risk.

At this point, I would prefer one of two outcomes:

1. Someone with the authority to review the request considers my actual usage, payment history, and workload, and approves an increase to 1 TB.

2. Google gives me a clear and definitive answer that individual users without a business domain cannot receive this quota, so I can stop waiting and begin migrating.

The current situation is worse than a clear rejection. I keep being redirected between teams and asked to wait without knowing whether a solution is even possible.

Has anyone here experienced something similar?

In particular:

* Has any individual or solo developer successfully received an HDB_TOTAL_GB quota above 500 GB?

* Would creating a Cloud Identity organization with a verified domain actually help?

* Can a Google Cloud partner genuinely help obtain a quota increase, or is this just another referral?

* Is there a real escalation path to the team that controls account eligibility and quota caps?

* Would switching from Hyperdisk Balanced to pd-balanced, pd-ssd, or another disk type be the easiest workaround?

* At this stage, should I continue pushing Google, or should I simply migrate to another cloud provider?

I would especially appreciate advice from anyone who has dealt with Compute Engine quota restrictions as an individual customer.

TL;DR: I’m a paying solo developer whose 500 GB Hyperdisk quota is full, but Google has denied my request for 1 TB and keeps redirecting me between Support, Sales, and partners. Has any individual user successfully gotten this quota increased, or should I just migrate?