#VertexAI
50 Vertex AI Interview questions and answers:

https://tapurl.to/vertexai
October 25, 2026 at 4:07 PM
📦 BerriAI / litellm
⭐ 60,459 (+95)
🗒 Python

The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]
GitHub - BerriAI/litellm: The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]
The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthr...
github.com
October 9, 2026 at 4:17 PM
今日のGitHubトレンド

BerriAI/litellm
LiteLLMは、OpenAI、Anthropic、Geminiなど100以上のLLMプロバイダーを、OpenAI形式の統一されたインターフェースで呼び出すためのオープンソースAIゲートウェイです。
Python SDKやプロキシサーバーとして利用でき、異なるプロバイダー間でのSDKや認証形式、リクエスト形式の差異を解消し、LLMの管理を簡素化することを目的としています。
GitHub - BerriAI/litellm: The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM] · GitHub
The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropi
github.com
October 9, 2026 at 5:35 PM
Gemini 4 Argon on Google Cloud: What Actually Changes for Platform Teams (2026)

Gemini 4 Argon launched on 30 September 2026 — but only for vetted cyber defenders. What platform teams on Google Cloud should change now: the 1M-token output…

#gemini4argon #vertexai #geminienterpriseagentplatform
Gemini 4 Argon on Google Cloud: What Actually Changes for Platform Teams (2026)
Gemini 4 Argon launched on 30 September 2026 — but only for vetted cyber defenders. What platform teams on Google Cloud should change now: the 1M-token output budget, Argon vs Gemini 3.8 Flash routing, agent guardrails, and the deadlines that land before Argon does.
www.alekseialeinikov.com
October 2, 2026 at 7:33 AM
Gemini 4 Argon auf Google Cloud: Was sich für Platform-Teams wirklich ändert (2026)

Gemini 4 Argon ist seit dem 30. September 2026 angekündigt — aber nur für geprüfte Cyber-Verteidiger verfügbar. Was Platform-Teams auf Google Cloud jetzt än…

#gemini4argon #vertexai #geminienterpriseagentplatform
Gemini 4 Argon auf Google Cloud: Was sich für Platform-Teams wirklich ändert (2026)
Gemini 4 Argon ist seit dem 30. September 2026 angekündigt — aber nur für geprüfte Cyber-Verteidiger verfügbar. Was Platform-Teams auf Google Cloud jetzt ändern sollten: Budget für 1 Mio. Output-Tokens, Routing zwischen Argon und Gemini 3.8 Flash, Leitplanken für Agenten und die Fristen, die vor Argon kommen.
www.alekseialeinikov.com
October 2, 2026 at 7:33 AM
The real question is whether Google and others rename it in their materials like they did with Lake Ontario. It's hard with VertexAI etc...
United States President Donald Trump issues executive order instructing federal government agencies to "use the terms 'Super Intelligence' and 'SI' in place of 'Artificial Intelligence' and 'AI' and will not acknowledge the usage of 'Artificial Intelligence' and 'AI' in any applicable setting."
Inaugurating The Era Of Super Intelligence
By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered: Section 1. Every child deserves a safe and nurturing home where they ...
www.whitehouse.gov
September 29, 2026 at 11:15 PM
VertexAI se lo han cargado para llamarlo Agent Platform es que yo me mato
September 23, 2026 at 2:20 PM
Firebase AI Logic in Angular: Client-Side Gemini Without a Custom Backend
Since I last wrote Building AI-powered e-commerce applications using Angular & Firebase AI Logic (formerly Vertex AI in Firebase) in 2025, so much has changed that this article may look like a complete rewrite. **Firebase AI Logic** is the successor to **Vertex AI in Firebase** (May 2025). This post covers the Angular setup only. For the full rename, new APIs, and migration from `firebase/vertexai`, see Vertex AI in Firebase is now Firebase AI Logic — What Actually Changed. Official Firebase docs cover each piece, but the order matters. This guide follows the sequence that actually works. We use the modular **`firebase` JS SDK + Angular DI**, so this works on Angular 18+. ## Why Firebase AI Logic in web apps Most Gemini tutorials show either **AI Studio + a raw API key** or a **custom Node/Python proxy** you host and secure yourself. Firebase AI Logic sits in between: Gemini runs from your **Angular app in the browser** , but requests go through Firebase's managed path and not a DIY backend you maintain. **What you get on web:** Benefit | Why it matters ---|--- **No custom AI backend** | Call `generateContent()` from a service; skip building Express/Cloud Functions just to hide an API key **App Check attestation** | Proves traffic comes from your real app (reCAPTCHA Enterprise in prod, debug tokens on localhost) — required for production AI Logic on web **One Firebase project** | Same Console for Hosting, Auth, Firestore, Remote Config, and AI monitoring **Gemini Developer API path** | `GoogleAIBackend()`: fast onboarding, Spark plan for prototypes, Console wizard provisions APIs for you **Upgrade path** | Switch to `AgentPlatformBackend()` later if you need enterprise Vertex/Agent Platform features; often a one-line backend change **Good fit for web apps:** * Shopping assistants, support chat, on-page copilots * Multi-turn chat and function calling (tools that call your existing Angular services) * Features where latency matters; inference starts from the client without an extra hop through your server **When to use something else:** * **Sensitive prompts or secrets** : keep those on the server (Genkit, Cloud Functions) * **Heavy RAG over private corpora** : server-side retrieval is usually safer and more flexible * **Billing/abuse control at the edge** : combine App Check enforcement with Firebase Console monitoring; add server gates for high-risk actions For ByteWise, the agent reads inventory and updates the cart via **function calling** ; still client-side AI Logic, with business logic in Angular services. This guide stops at your first `generateContent()`; the ByteWise repo goes further. ## What you are building Firebase Console (AI Logic + App Check + API keys) ↓ firebase.config.ts ← credentials firebase-ai.ts ← initializeApp + App Check + getAI (providers) app.config.ts ← register providers once ai.service.ts ← inject FIREBASE_AI → generateContent() You only **create two new Firebase-specific files** beyond a standard Angular app: File | Purpose ---|--- `src/environments/firebase.config.ts` | `firebaseConfig` + reCAPTCHA site key `src/app/firebase/firebase-ai.ts` | Bootstrap: tokens, App Check, `provideFirebaseAI()` Everything else is small edits to files you already have (`app.config.ts`, `index.html`, one service). ## Prerequisites * Angular **18+** (standalone, `ApplicationConfig` style) * Node.js LTS * A Google account and a Firebase project * **`firebase` >= 12.19.0** (required for current Gemini + App Check behaviour) npm install firebase@^12.19.0 ## Part 1: Firebase Console (do this before writing any code) ### 1. Create a project and web app 1. Firebase Console → **Create a new Firebase project** 2. **Add app** → **Web** (`</>`) 3. Copy the `firebaseConfig` object — you will paste it in Part 2 ### 2. Enable Firebase AI Logic (early) 1. **AI Services** → **AI Logic** → **Get started** 2. Choose **Gemini Developer API** and finish the setup wizard. Ensure to enable the APIs recommended and AI monitoring. Wait **5–10 minutes** , then in Google Cloud console (same project ID as Firebase): **APIs & Services** → **Enabled APIs & services** → scroll past the charts to the API table → use **Filter** to confirm **Firebase AI Logic API** and **Gemini API** are listed. Firebase's wizard labels the second one **Gemini Developer API** ; GCP now shows **Gemini API** (`generativelanguage.googleapis.com`). You may also see **Gemini for Google Cloud API** when filtering `Gemini` — that is for Gemini inside the Cloud Console and is not required for Firebase AI Logic in your Angular app. Skipping this step is the most common cause of **403 "The caller does not have permission"** later. ### 3. reCAPTCHA Enterprise + App Check **3a. Create a reCAPTCHA Enterprise key** 1. Google Cloud (same project) → **Security** → **Fraud Defense** → Enable **reCAPTCHA Enterprise API** 2. Select **Keys** on the tab → **Create key** → Input any **Display name** of your choice → **Website** 3. **Domains:** add domains that production only. **Do not** add `localhost`. For example: * `<project-id>.web.app` * `<project-id>.firebaseapp.com` * Any custom domain you use later 4. Click on **Create Key** 5. After creation, copy the **site key**. This key starts with something like `6L...` **3b. Register App Check** 1. Firebase Console → **Security** → **App Check** → Click on the **Apps** tab → Click on your web app → **reCAPTCHA Enterprise** → paste site key (from Step 3a). You can leave the default configurations as they are. 2. **APIs** tab → **Firebase AI Logic**. You will find Monitoring and Baseline Protection have already been enabled (`Basic - Enforced`). However, you can set up additional checks if you want such as **Replay Protection**. ### 4. Browser API key restrictions 1. Google Cloud → **APIs & Services** → **Credentials** 2. Open the **Browser key** matching your `firebaseConfig.apiKey` 3. **API restrictions:** You will find about 25 APIs already allowed on that project and ensure you allow **Firebase AI Logic API** and **Firebase App Check API** if not already allowed. 4. **Application restrictions:** **None** while developing on `localhost` ## Part 2: Angular configuration ### Step 1: Generate Angular environment variables files You can generate your Angular environment files using the command: ng generate environments ### Step 2: `src/environments/firebase.config.ts` Inside the `environment` folder, create a file with the name `firebase.config.ts` and copy the code snippet below. Replace the `YOUR_` values with the actual values from your Firebase project **Settings** (scroll all the way down to the **SDK setup and configuration** section). The `recaptchaEnterpriseSiteKey` is the same as the one in **Part 1: Step 3a** : Google Cloud (same project) → Security → Fraud Defense → Keys → Copy the already created key. import type { FirebaseOptions } from 'firebase/app'; export const firebaseConfig: FirebaseOptions = { apiKey: 'YOUR_API_KEY', authDomain: 'YOUR_PROJECT.firebaseapp.com', projectId: 'YOUR_PROJECT_ID', storageBucket: 'YOUR_PROJECT.firebasestorage.app', messagingSenderId: 'YOUR_SENDER_ID', appId: 'YOUR_APP_ID', }; export const recaptchaEnterpriseSiteKey = 'YOUR_RECAPTCHA_SITE_KEY'; ### Step 3: `src/environments/environment.model.ts` Create `environment.model.ts` — one shared type for production and development. `appCheckDebugToken` is **optional** so production omits it, while `firebase-ai.ts` can still reference `environment.appCheckDebugToken` without IDE `TS2339` errors (your editor type-checks `environment.ts`, not the dev file Angular swaps in at build time). import type { FirebaseOptions } from 'firebase/app'; export interface AppEnvironment { production: boolean; firebaseConfig: FirebaseOptions; recaptchaEnterpriseSiteKey: string; /** Dev/CI only — set in `environment.development.ts`, omit in `environment.ts` */ appCheckDebugToken?: boolean | string; } ### Step 4: `environment.ts` and `environment.development.ts` **`src/environments/environment.ts`** (production — used by `ng build`): import { firebaseConfig, recaptchaEnterpriseSiteKey } from './firebase.config'; import type { AppEnvironment } from './environment.model'; export const environment: AppEnvironment = { production: true, firebaseConfig, recaptchaEnterpriseSiteKey, }; **`src/environments/environment.development.ts`** (local dev — swapped in by `ng serve`): import { firebaseConfig, recaptchaEnterpriseSiteKey } from './firebase.config'; import type { AppEnvironment } from './environment.model'; export const environment: AppEnvironment = { production: false, firebaseConfig, recaptchaEnterpriseSiteKey, appCheckDebugToken: true, }; Confirm `angular.json` has `fileReplacements` under the **development** build configuration ( `ng generate environments` usually adds this): "fileReplacements": [ { "replace": "src/environments/environment.ts", "with": "src/environments/environment.development.ts" } ] Always import the alias path in app code — **never** `environment.development` directly: import { environment } from '../../environments/environment'; > **NOTE:** On **`ng serve`** , the CLI substitutes `environment.development.ts` wherever you import `environment.ts`. Production builds use the real `environment.ts` with no debug token. ### Step 5: `src/app/firebase/firebase-ai.ts` Create a file with the name `firebase-ai.ts` under a folder `firebase`. import { inject, InjectionToken, makeEnvironmentProviders, PLATFORM_ID, provideAppInitializer, type EnvironmentProviders, } from '@angular/core'; import { isPlatformBrowser } from '@angular/common'; import { getAI, GoogleAIBackend, type AI } from 'firebase/ai'; import { initializeApp, type FirebaseApp } from 'firebase/app'; import { CustomProvider, initializeAppCheck, ReCaptchaEnterpriseProvider, type AppCheck, } from 'firebase/app-check'; import { environment } from '../../environments/environment'; export const FIREBASE_AI = new InjectionToken<AI>('FIREBASE_AI'); const FIREBASE_APP_CHECK = new InjectionToken<AppCheck>('FIREBASE_APP_CHECK'); const firebaseApp = initializeApp(environment.firebaseConfig); function initAppCheck(firebaseApp: FirebaseApp, platformId: object): AppCheck { const isBrowser = isPlatformBrowser(platformId); if (isBrowser && !environment.production) { (globalThis as typeof globalThis & { FIREBASE_APPCHECK_DEBUG_TOKEN?: boolean | string }) .FIREBASE_APPCHECK_DEBUG_TOKEN ??= environment.appCheckDebugToken ?? true; } if (!isBrowser) { return initializeAppCheck(firebaseApp, { provider: new CustomProvider({ getToken: async () => ({ token: 'ssr-placeholder', expireTimeMillis: Date.now() + 60 * 60 * 1000, }), }), isTokenAutoRefreshEnabled: false, }); } return initializeAppCheck(firebaseApp, { provider: new ReCaptchaEnterpriseProvider(environment.recaptchaEnterpriseSiteKey), isTokenAutoRefreshEnabled: true, }); } export function provideFirebaseAI(): EnvironmentProviders { return makeEnvironmentProviders([ { provide: FIREBASE_APP_CHECK, useFactory: (platformId: object) => initAppCheck(firebaseApp, platformId), deps: [PLATFORM_ID], }, { provide: FIREBASE_AI, useFactory: (_appCheck: AppCheck): AI => getAI(firebaseApp, { backend: new GoogleAIBackend(), useLimitedUseAppCheckTokens: environment.production, }), deps: [FIREBASE_APP_CHECK], }, // Eager-init App Check on startup — prints the debug token in DevTools // immediately so you can register it in Firebase Console before Part 3. provideAppInitializer(() => { inject(FIREBASE_APP_CHECK); }), ]); } ### Step 6: `src/app/app.config.ts` import { ApplicationConfig } from '@angular/core'; import { provideRouter } from '@angular/router'; import { routes } from './app.routes'; import { provideFirebaseAI } from './firebase/firebase-ai'; export const appConfig: ApplicationConfig = { providers: [ provideRouter(routes), provideFirebaseAI(), ], }; ### Step 7: Localhost debug token — `src/index.html` Add this inside the `<body>` element, right before `<app-root>`. <script> if (location.hostname === 'localhost' || location.hostname === '127.0.0.1') { self.FIREBASE_APPCHECK_DEBUG_TOKEN = true; } </script> ### Step 8: Run locally and capture the debug token ng serve Open **http://localhost:4200** (the URL the Angular CLI prints in the terminal). **Where the token appears:** the Firebase SDK logs it when `initializeAppCheck()` runs with the debug provider active. Open DevTools → **Console** : Search/filter for **`AppCheck debug token`** or **`debug token`** You should see a line similar to: Firebase App Check debug token: "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" Copy the UUID (without quotes), then register it: Firebase Console → **App Check** → Apps → your web app → **⋮** menu → **Manage debug tokens** → **Add debug token** → Give the debug token any name of your choice → paste → save → hard-refresh the app (`Ctrl+Shift+R`). > **NOTE:** The debug token is **not** in `environment.ts`. It's **minted by the SDK** and printed in the Console once App Check initializes on the client. The debug token is ONLY for use during development. Production must use reCAPTCHA, not debug tokens. ## Part 3: Your first `generateContent` ### Step 1: `src/app/services/ai.service.ts` import { inject, Injectable } from '@angular/core'; import { getGenerativeModel } from 'firebase/ai'; import { FIREBASE_AI } from '../firebase/firebase-ai'; @Injectable({ providedIn: 'root' }) export class AiService { private readonly ai = inject(FIREBASE_AI); async ask(prompt: string): Promise<string> { const model = getGenerativeModel(this.ai, { model: 'gemini-3.5-flash' }); const result = await model.generateContent(prompt); return result.response.text(); } } ### Step 2: Call it from a component Generate a dedicated **AI Demo** component with the Angular CLI, or wire the call into your root **`App`** component. ng generate component ai-demo --inline-template --skip-tests import { Component, inject, signal } from '@angular/core'; import { AiService } from '../services/ai.service'; @Component({ selector: 'app-ai-demo', template: ` <button (click)="run()" [disabled]="loading()"> {{ loading() ? 'Please wait…' : 'Ask Gemini' }} </button> @if (error()) { <p role="alert">{{ error() }}</p> } @if (reply()) { <pre>{{ reply() }}</pre> } `, styleUrl: './ai-demo.scss', }) export class AiDemo { private readonly ai = inject(AiService); readonly loading = signal(false); readonly reply = signal(''); readonly error = signal(''); async run(): Promise<void> { this.loading.set(true); this.error.set(''); this.reply.set(''); try { this.reply.set(await this.ai.ask('Say hello in one sentence.')); } catch (err) { this.error.set( err instanceof Error ? err.message : 'Something went wrong. Check the console.', ); } finally { this.loading.set(false); } } } Render it from `src/app/app.html`: <app-ai-demo /> Import **`AiDemo`** in `src/app/app.ts` — Angular components are standalone by default and must be listed in the parent `imports` array: import { Component } from '@angular/core'; import { AiDemo } from './ai-demo/ai-demo'; @Component({ selector: 'app-root', imports: [AiDemo], templateUrl: './app.html', styleUrl: './app.scss', }) export class App {} Without that import, `<app-ai-demo />` will fail at compile time with an unknown element error. Run `ng serve` and click on the **Ask Gemini** button in the rendered UI on **http://localhost:4200** ## Part 4: Verify it worked Click **Ask Gemini**. If you get text back — not a **403** — your setup works. That is the pass/fail check. On first load, the console should show no `App Check token fetch failed`, and you should have registered the debug token from Part 2, Step 8. Optional: in DevTools → **Network** , filter `firebasevertexai` and confirm a **POST** to `:generateContent` returns **200**. Do not worry if you cannot spot `X-Firebase-AppCheck` — the header is easy to miss, and Gemini succeeding is what matters. **403 The caller does not have permission?** Re-run **AI Logic → Get started** , confirm the debug token is registered, confirm the Browser key allowlist includes **Firebase AI Logic API** + **Firebase App Check API** , then wait a few minutes and hard-refresh. ## Part 5: What you can add next Feature | SDK entry point ---|--- Multi-turn chat | `startChat()` on a generative model Function calling | `tools` + `FunctionCallingConfig` on the model Streaming | `generateContentStream()` System instructions | `systemInstruction` in model config For a full function-calling example, see ByteWise ai.service.ts. ## Conclusion We are at an inflection point where developers can build intelligent web experiences, powered by Large Language Models. Firebase AI Logic provides this infrastructure with a free tier on the Gemini Developer API and Agent Platform Gemini API for enterprise-level use cases. If you want these capabilities where latency matters, Firebase AI Logic will be your best bet, while offering you security and abuse prevention for your production apps. To see Firebase AI Logic in action in e-commerce applications, check out Bytewise Shop, a fictional tech gadget shop I built to demonstrate the use of tool calling on actions such as **add to cart** , **check inventory** , **clearing cart** , all using **natural language**. ## File checklist * [ ] `src/environments/firebase.config.ts` — **new** * [ ] `src/environments/environment.model.ts` — **new** * [ ] `src/environments/environment.ts` — production values * [ ] `src/environments/environment.development.ts` — add `appCheckDebugToken` * [ ] `src/app/firebase/firebase-ai.ts` — **new** * [ ] `src/app/app.config.ts` — add `provideFirebaseAI()` * [ ] `src/index.html` — localhost debug script * [ ] `src/app/services/ai.service.ts` — **new** * [ ] `AiDemo` component + import in `app.ts` ## Related links * Vertex AI in Firebase is now Firebase AI Logic — What Actually Changed — companion article (rename, migration, what's new) * Firebase AI Logic overview * App Check with AI Logic (web)
dev.to
September 16, 2026 at 5:39 PM
Yes, but what's the competitive moat? Google vertexAI, or AWS Bedrock (conceptually, idk what AWS is doing here anymore) seem just as capable to roll this up - if they quit stepping on their dicks.
August 24, 2026 at 2:01 AM
so...my program has potential now
i have extreme pride in my solution a VertexAi generated program, a CAD workstation webapp named GeoParametric3d
@ lftr.biz/cad
A #python program that rocks!!! G(x,y,z)

ie...many uses however upload upto 200 mb step right now.for sure
lftr.biz
August 20, 2026 at 3:19 PM
Claude 3 Haiku reached end of life on Vertex AI in August 2026. Model deprecation cycles in the AI era run in months not years so pin your model versions and budget for re-integration every quarter
#VertexAI #Deprecation #AI
August 20, 2026 at 3:40 AM
It seems like the new Gemini 2.0 Flash model is going to be cheap! Latest LMARENA data confirms top-tier performance at budget pricing. 💰

🪦RIP to the profit margins of the big competitors!

Can’t wait to see this in VertexAI.

#Gemini2 #AI #ML #google #openai
December 16, 2024 at 11:43 AM
Made a simple agent with VertexAI that allows you to ask about a date and it will tell you if there was a Google update.

Anyone want to test it out? Click the conversation button on the bottom right of this post.

www.mariehaynes.com/resources/al...
February 14, 2025 at 6:37 PM
🤖 Comment utiliser Vertex AI Agent Engine sur Google Cloud pour déployer facilement vos agents (ADK, LangChain, etc.).

J'explique tout dans mon dernier article : déploiement via ADK ou via le SDK Vertex AI pour les frameworks externes.

👉 dev.to/zenika/deplo...

#GenAI #VertexAI #GoogleCloud
🤖 Déployer son agent sur Google Vertex AI Agent Engine
Google Cloud a récemment introduit Vertex AI Agent Engine, une plateforme intégrée à l’écosystème...
dev.to
November 12, 2025 at 12:01 PM
And an example from that VertexAI-KMP sample 😀 (using Imagen 4 here) #GoogleIOConnect
June 25, 2025 at 1:06 PM
Seemed appropriate that following of my KMP/CMP samples are first to be updated to Kotlin 2.2.0 + related dependencies! #GoogleIOConnect

✅ GeminiKMP github.com/joreilly/Gem...
✅ VertexAI-KMP github.com/joreilly/Ver...
June 25, 2025 at 8:54 AM
Which is which though? 😀 (from github.com/joreilly/Ver...) #CMP #KMP #VertexAI
March 25, 2025 at 1:01 PM
Thinking about using this example image for one of slides for talk at @androidmakers.bsky.social in a few weeks time #VertexAI😀🇫🇷
March 22, 2025 at 12:01 PM
Android working now as well (thanks @thatfire.dev for pointer about format of data returned on Android) and changes merged to github.com/joreilly/Ver... (you'll need your own google-services/API key etc if using Imagen...seems different billing requirement for that)
GitHub - joreilly/VertexAI-KMP-Sample: Compose Multiplatform sample that uses the Firebase Vertex AI SDKs
Compose Multiplatform sample that uses the Firebase Vertex AI SDKs - joreilly/VertexAI-KMP-Sample
github.com
March 20, 2025 at 8:30 PM
Gemini's TTS models block the generation of audio of a conversation that includes Javier Milei or Russia and the word "nuclear". And this is how Gemini itself justifies it #genai #tts #vertexai #ai
January 22, 2026 at 5:32 PM