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What is Laguna XS.2?

Laguna XS.2 stands out as Poolside's groundbreaking open-weight coding model, noted for being the lightest and fastest in the Laguna lineup. Equipped with a staggering 33 billion parameters organized in a Mixture of Experts structure, of which 3 billion are active, this model has undergone extensive training in-house utilizing 30 trillion tokens. As the most recent generation model available to the public, it features a second-generation architecture and represents Poolside's first open-weight release, benefiting from lessons learned during the Laguna M.1 training process, which utilized synthetic data and reinforcement learning. Tailored specifically to optimize agentic coding workflows, Laguna XS.2 is exceptional in coding, acting, and rapid iteration, particularly within Poolside's coding agent ecosystem. This model is especially beneficial for developers and teams in need of a lightweight and efficient coding solution, as opposed to more complex frontier systems. Released under the flexible Apache 2.0 license, it enables the community to evaluate, refine, quantize, and build upon its weights, fostering an environment of collaborative development. Ultimately, Laguna XS.2 not only serves as a powerful tool for agentic coding but also promotes creativity and experimentation among its users, allowing for a diverse range of applications and enhancements.

What is Antares?

Antares is a collection of open-weight security small language models crafted to detect vulnerabilities within large codebases. Featuring models such as Antares-350M and Antares-1B, these tools can be deployed locally or on-site, ensuring that proprietary source code remains secure while also reducing both inference expenses and runtime. The procedure starts with an outline of the vulnerability, which may include an advisory or a CWE category; from there, the model embarks on a detailed investigation similar to that of a human analyst, methodically looking for relevant code patterns, scrutinizing possible files, integrating new data, and adjusting its strategy when certain paths appear unproductive. This method allows the model to concentrate its resources on the files most likely to contain the identified flaws. In the end, Antares produces a prioritized list of source files that may be vulnerable, accompanied by a comprehensive trail of the exploration process that led to these conclusions, thereby simplifying the review and prioritization for teams. Furthermore, this functionality not only accelerates the vulnerability assessment process but also significantly strengthens the overall security framework of the development environment, fostering a culture of proactive security measures. Ultimately, organizations can benefit from improved efficiency and effectiveness in managing their code vulnerabilities.

Media

Media

Integrations Supported

Agent Client Protocol (ACP)
Claude Code
Cline
Hermes Agent
Hugging Face
IntelliJ IDEA
Kilo Code
Nous Portal
Ollama
OpenAI Codex
OpenClaw
OpenCode
OpenRouter
Poolside
Roo Code
Visual Studio
Visual Studio Code
Zed

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

Poolside

Date Founded

2023

Company Location

United States

Company Website

www.poolside.ai/models

Company Facts

Organization Name

Cisco

Date Founded

1984

Company Location

United States

Company Website

blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Open-Weight AI Models

Not specified

Categories and Features

AI Models

Not specified

AI Security

Not specified

Open-Weight AI Models

Not specified

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