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The Hacker News

Google Launches Gemini 3.5 Flash Cyber AI to Find and Fix Software Vulnerabilities

Google's DeepMind on Tuesday announced the release of Gemini 3.5 Flash Cyber, a specialized artificial intelligence (AI) model built atop 3.5 Flash that's designed to discover, validate, and patch vulnerabilities quickly and efficiently. According to the tech giant, the model will be exclusively available to governments and trusted partners via CodeMender as part of a limited-access pilot

The Hacker NewsRead original
ExternalSoftware Engineering
GitHub Changelog

Gemini 3.6 Flash is now available in GitHub Copilot

Gemini 3.6 Flash, Google’s latest Flash model, is now rolling out in GitHub Copilot. It is designed for web and app development, coding, and longer-horizon agentic tasks. It has configurable… The post Gemini 3.6 Flash is now available in GitHub Copilot appeared first on The GitHub Blog.

GitHub ChangelogRead original
ExternalTechnology Trends
Wired Business

Nvidia Wants to Own Every Chip Inside AI Data Centers

Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.

BusinessBusiness / Artificial IntelligenceBusiness / Big Tech
Wired BusinessRead original
ExternalSoftware Engineering
DZone

Spec-Driven Development Renamed an Old Problem; It Didn't Solve It

Spec Kit, OpenSpec, BMAD, Kiro — all of it is built on the assumption that a spec can stay the source of truth. It can't, for the same reason design docs and wikis never stayed current either. I think the interesting unsolved problem in this space isn't "more rigorous specs," it's "specs that don't require a human to remember to update them." Curious if people running these in production agree. The Pitch Everyone's Making Right Now Spec-driven development has become the default answer to "AI age

DZoneRead original
ExternalCloud
Google Cloud Blog

Now in preview: Find and fix software vulnerabilities with CodeMender

As adversarial AI threats accelerate attacks on code, security teams must counter them with machine-speed defenses that can automate code remediation and fight AI with AI. CodeMender is our managed code security agent, and starting today, we're bringing its code scanning and remediation capabilities directly to you in preview. CodeMender offers access to our generally available models via Gemini Enterprise Agent Platform, or it can be deployed as a core component of AI Threat Defense.  CodeMende

AI & Machine LearningSecurity & Identity
Google Cloud BlogRead original
ExternalAI
NVIDIA Technical Blog

Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale.... What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale. These factories are now tasked with powering agentic workflows that reason, plan, use tools, verify intermediate results, and execute complex multistep tasks across vast context

NVIDIA Technical BlogRead original
ExternalAI
NVIDIA Technical Blog

NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with... Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with databases, and analyze results before returning information to the model. As these loops run concurrently across an AI factory, CPU performance increasingly shapes both per-agent

NVIDIA Technical BlogRead original
ExternalAI
NVIDIA Technical Blog

Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token... Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token falls, communication increasingly determines how efficiently models scale across thousands of GPUs. NVIDIA GB300 NVL72 set a world record for pre-training DeepSeek-V3 671B at

NVIDIA Technical BlogRead original

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