Loading video...

Video Failed to Load

Go Home

.Veria Labs' AI agents continuously pentest your code. Companies ship code daily but test for security once a year. Traditional pentests take weeks and cost $30K+. Veria's agents run 24/7 and find vulnerabilities human pentesters miss. Congrats on the launch, jayden, SuperBeetleGamer, and Joana!

14,409 views • 1 year ago •via X (Twitter)

7 Comments

The Longer Game's profile picture
The Longer Game1 year ago

@verialabs That’s a strong shift. Continuous AI pentesting will make shipping code faster and far more secure.

Jawaad Arif's profile picture
Jawaad Arif1 year ago

@verialabs @ycombinator, this is a game-changer for security testing.

Juicy Lucy's profile picture
Juicy Lucy1 year ago

@verialabs Continuous pentesting is crucial, especially when code ships daily. I believe AI agents like Shade Agents can enhance security with 24/7 monitoring and NEAR chain signatures. What do you think about AI-driven security? $SHITZU

Anuj kumar's profile picture
Anuj kumar1 year ago

@verialabs Congrats on the launch! 24/7 AI pentesting sounds like a real game changer

Chirag S kotian's profile picture
Chirag S kotian1 year ago

@verialabs Pentesting sounds kind of interesting,let's connect 😁

Lisa Brown's profile picture
Lisa Brown1 year ago

@verialabs "AI agents: 24/7 security, no more missed bugs!"

Anuj kumar's profile picture
Anuj kumar1 year ago

@verialabs Continuous AI-powered pentesting feels like the future of security fast, cost-effective, and always on.

Related Videos

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 views • 1 year ago