Gemini

SAP 2024 outlook: 5 predictions for customers

February 12, 2024: As SAP continues to position itself as a leader in generative AI and innovative technologies, customers must prepare to navigate new service offerings and an inevitable move to SAP RISE.

SAP’s new generative AI pricing: Neither transparent nor explainable yet

October 12, 2023: The ERP vendor is adding a new pricing tier to its Rise with SAP offering with an opaque mix of bundled and usage-based pricing for generative AI functionality.

SAP prepares to add Joule generative AI copilot across its apps

SAP is expanding its generative AI copilot Joule to include AI agents. Deeply embedded in SAP systems, the company’s agents aim to solve increasingly complex tasks.

Kimi K3 : 3.5 de Gemini retardé, écrasant ARC-AGI 3 :

TLDR Data17 juil.

xAI chaos, Gemini retarde, gagnant jugement

TLDR Data17 juil.

Using the Best of Google AI to Set the Standard in Mobile Scam Protection

Android continues to set the standard in mobile scam protections by leveraging advanced AI to identify and intercept threats as they happen. As scammer’s strategies shift, we remain committed to developing equally adaptive and intelligent defenses. Our goal is to provide you with peace of mind so you can continue to connect and communicate with confidence, knowing our multi-layered defenses are there to help protect your financial and personal data against mobile scams. 1: This total comprises

1Password’s key findings: Agent security testing

No AI model is safe enough out of the box. Averaged across three runs, safety scores ranged from 35% (Gemini 2.5 Flash) to 92% (Claude Opus 4.6). Six of eight models scored below 82%. Every model committed critical failures in every run. The cheapest AI model was the most dangerous. Gemini 2.5 Flash averaged 20 critical failures per run across 30 scenarios. GPT-4.1 and GPT-4.1 Mini were close behind at 19 and 18, respectively. These models forwarded passwords to external contractors, typed crede

The SCAM leaderboard

ModelOut of the Box± Avg Critical FailuresSkill± Avg Critical FailuresImprovementGemini 3 Flash75.6%2.199.3% 0.7+23.7Claude Opus 4.692.4%0.598.3% 0.2+5.9Claude Sonnet 449.4%1.798.1% 0.7+48.7Claude Haiku 4.565.5%5.497.8% 1.2+32.3GPT-5.281.0%1.496.6% 1.2+15.5GPT-4.137.9%2.496.2% 0.7+58.3Gemini 2.5 Flash34.9%3.494.8% 1.4+59.9GPT-4.1 Mini35.8%0.394.7% 2.9+58.8 The baseline rankings follow the pattern you'd expect: larger, more capable models score higher. But the skill rankings tell a different stor

The generative media models

Each film combined multiple Google AI models. The agents called them through a shared CLI toolkit called genmedia: Gemini image generation (Nano Banana) produced character reference sheets, storyboard frames, and scene compositions. The agents kept characters visually consistent across a film through reference chaining: they generated headshots first, then used those as input for body sheets, then used body sheets as input for scene tests. Each generation call included these accumulated referenc

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The Making Of — a documentary about AI agents competing in a generative media hackathon.All media was produced using Google's generative AI models including ...

Background on vLLM

vLLM is a high-performance, open source inference engine for large language models. It is designed for serving many concurrent requests efficiently in a cluster environment. An important characteristic of vLLM for this use case is that it exposes an OpenAI-compatible REST API. Anything that already talks to the OpenAI API (LangChain, LlamaIndex, or your own code that calls the OpenAI SDK) can be pointed at a self-hosted vLLM instance with nothing more than a URL change. That compatibility is wha

Finding 3: Provider-native retrieval already leads the vector databases

OpenAI file search and vertex AI search top the dedicated tools We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists. The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one en

1Password’s key findings: Agent security testing

No AI model is safe enough out of the box. Averaged across three runs, safety scores ranged from 35% (Gemini 2.5 Flash) to 92% (Claude Opus 4.6). Six of eight models scored below 82%. Every model committed critical failures in every run. The cheapest AI model was the most dangerous. Gemini 2.5 Flash averaged 20 critical failures per run across 30 scenarios. GPT-4.1 and GPT-4.1 Mini were close behind at 19 and 18, respectively. These models forwarded passwords to external contractors, typed crede

The SCAM leaderboard

ModelOut of the Box± Avg Critical FailuresSkill± Avg Critical FailuresImprovementGemini 3 Flash75.6%2.199.3% 0.7+23.7Claude Opus 4.692.4%0.598.3% 0.2+5.9Claude Sonnet 449.4%1.798.1% 0.7+48.7Claude Haiku 4.565.5%5.497.8% 1.2+32.3GPT-5.281.0%1.496.6% 1.2+15.5GPT-4.137.9%2.496.2% 0.7+58.3Gemini 2.5 Flash34.9%3.494.8% 1.4+59.9GPT-4.1 Mini35.8%0.394.7% 2.9+58.8 The baseline rankings follow the pattern you'd expect: larger, more capable models score higher. But the skill rankings tell a different stor

From tricycle to Porsche: Salesforce rebuilt Slackbot from the ground up

Salesforce a entièrement reconstruit Slackbot en le transformant d'un outil algorithmique simple en une solution sophistiquée basée sur un grand modèle de langage. La nouvelle version intègre des capacités de recherche avancées et accède à des données d'entreprise variées (enregistrements Salesforce, Google Drive, calendriers, conversations Slack). Malgré cette refonte technique majeure, Salesforce a conservé le nom Slackbot pour préserver la reconnaissance de la marque auprès des utilisateurs.

Apple et les interfaces homme-machine

Apple cherche à renforcer sa position dans les interfaces homme-machine en développant une commande vocale chuchotée et en intégrant des modèles d'IA plus avancés à Siri, notamment via un partenariat avec Google autour de Gemini. Cette stratégie vise à combler le retard de l'assistant face aux solutions concurrentes dopées à l'IA générative. En combinant une interface vocale innovante avec une intelligence accrue, Apple entend retrouver son avantage historique dans l'ergonomie et l'utilisabilité.

Discovery — now free for everyone

Firewall for AI discovers and protects your public LLM-powered applications, and is seamlessly integrated with Cloudflare WAF. Join the beta now and take control of your generative AI security.

“By economic and engineering measures, generative AI might be the worst technology ever deployed.”

A shockingly inefficient trillion-dollar project

Why Gemini Enterprise app + BigQuery is a game-changer

Learn how Cloud Logging uses the log router and log sinks to route log entries to various destinations, such as storage buckets.

Empower your organization with the Gemini Enterprise app and your administrators with BigQuery

Discover, create, share, and run AI agents all in one secure platform, bringing the best of Google AI to every employee, for every workflow.

In-database pre-processing with Gemini

AlloyDB AI bypasses these workarounds — and their limits — by introducing native, in-database AI Functions like ai.generate(). This allows you to call Gemini directly from SQL, keeping your data and intelligence in one place. This approach provides three core advantages: No data movement: All text preprocessing and segmentation happen directly within the database engine. This minimizes network latency and keeps your data protected within your database boundaries. In-database intelligence: You