Laguna by Poolside

As a freelancer navigating the fast-paced world of AI-powered development, I'm always on the lookout for tools that genuinely push the boundaries of what's possible. Lately, there's been a lot of buzz around agentic AI, where models don't just generate text or code, but can actually plan, execute, and iterate on complex tasks. This is exactly why "Laguna by Poolside" caught my attention.

Recently launched, Poolside AI's Laguna models promise to transform how we approach software engineering. I've taken a deep dive into what this new offering brings to the table, and I'm ready to share my findings from a freelancer's perspective.

What is Laguna by Poolside and What Core Problem Does it Solve?

Laguna by Poolside isn't just another AI coding assistant; it's a suite of foundation models designed specifically for "agentic coding and long-horizon work." At its heart, Poolside AI, founded in early 2023 by GitHub's former CTO Jason Warner and software entrepreneur Eiso Kant, aims to build the most capable AI for software development.

The core problem Laguna tackles is significant: the ever-growing demand for high-quality code far outstrips the supply of skilled developers. Plus, a good chunk of a developer's time often goes into repetitive, time-consuming tasks like connecting APIs, handling test coverage, or writing simple application logic, rather than strategic problem-solving. Laguna steps in to bridge this gap, enabling AI to act more like a junior developer who can "think" at a higher level, plan, and execute complex coding tasks autonomously or semi-autonomously.

For freelancers like me, this means less time spent on boilerplate code or tedious debugging, and more time focusing on the creative and architectural aspects of a project. It’s about making software development more efficient and effective, especially for those complex, multi-step projects that require sustained effort and multiple iterations.

How Does Laguna Work?

What makes Laguna stand out is its unique approach to AI model development. Unlike many tools that fine-tune existing general-purpose large language models (LLMs) for coding, Poolside AI builds its own proprietary foundation models from the ground up. This allows them to specifically optimize these models for software engineering tasks.

The secret sauce is their proprietary training methodology: Reinforcement Learning from Code Execution Feedback (RLCEF). Instead of just passively consuming vast amounts of code, Laguna models actively learn by coding themselves. They generate code, execute it in sandboxed environments, and then receive objective feedback based on real outcomes—like whether the code compiles, if unit tests pass, or how well it performs. This continuous, active learning process helps the AI become more robust and reliable, effectively learning by doing, much like a human developer would.

Poolside also uses a specialized infrastructure called the "Model Factory" and internal software like "Titan" and the "Muon optimizer" to rapidly build, train, and evaluate these models. This ensures their models are constantly improving and can handle the complexities of real-world coding environments.

The "agentic" aspect means these models aren't just spitting out code snippets. They are designed as single and multi-agent systems that can plan out a series of steps, use various tools (like compilers, debuggers, or external APIs), and execute those plans within controlled environments. This capability is governed by policies and end-to-end traces, providing a clear audit trail of the AI's actions.

Key Features for Freelancers

Laguna by Poolside brings several compelling features that can significantly impact a freelancer's workflow:

• Laguna M.1: The Powerhouse for Complex Projects

  This is Poolside's flagship model, a proprietary 225-billion parameter Mixture of Experts (MoE) model with 23 billion active parameters. It's built for the most demanding software engineering problems, especially those requiring deep reasoning and extensive planning. As a freelancer, if I'm tackling a massive refactoring project, designing a complex system architecture, or debugging a tricky, long-standing bug across multiple files, Laguna M.1 is designed to be my co-pilot. Its ability to understand the bigger picture and handle "long-horizon" tasks is a game-changer for large, intricate projects.

• Laguna XS.2: Agile and Accessible Agentic Coding

  For more agile, local agentic coding tasks, Laguna XS.2 is a fantastic option. It's an Apache 2.0 open-licensed 33-billion parameter MoE model with 3 billion active parameters, making it Poolside's lightest and fastest agentic coding model. This model is engineered for efficiency and community innovation. For individual freelancers or small teams, XS.2 can be downloaded and run locally, making it perfect for rapid prototyping, smaller feature development, or experimenting with agentic workflows on a single GPU.

• Massive 256K Context Window

  Both Laguna M.1 and XS.2 boast an impressive 256K context window, capable of handling up to 32K output tokens. This is a huge deal for coding. Anyone who's worked on a large codebase knows how quickly context can get lost with smaller models. This extensive context window means the AI can understand entire projects, multiple files, and long conversation histories without losing track, which is critical for successful long-horizon refactors and multi-file editing tasks. It truly enables the AI to "see" and reason about the whole problem.

• Seamless Developer Tool Integrations

  Poolside understands that developers have existing workflows. Laguna models integrate directly into popular Integrated Development Environments (IDEs) like VS Code, Visual Studio, JetBrains, Zed, and Neovim, as well as command-line interfaces (CLIs) and even CI/CD pipelines. This means I can leverage Laguna's intelligence without having to constantly switch tools, keeping my flow state intact. They also offer a "pool" terminal-based coding agent and a web-based "shimmer" environment for on-the-go coding.

• Enterprise-Grade Security & Data Privacy

  While often associated with large organizations, security and data privacy are equally important for freelancers, especially when working with sensitive client projects. Poolside emphasizes an enterprise-first design with options for on-premises, Virtual Private Cloud (VPC), or workstation deployments. This ensures that client data never leaves your control. They also explicitly state that their training data excludes copyleft-licensed code, which is a significant ethical and legal consideration for many commercial projects.

Pricing

Poolside AI primarily targets enterprise clients, offering custom, pay-per-use pricing. This means the cost depends on factors like the volume of code processed, the type of GPU utilized, and the level of post-sales support required. For large organizations, Poolside's "Forward Deployed Research Engineers" even embed with client teams, taking joint responsibility for project outcomes.

However, here’s the exciting part for freelancers and the broader developer community: both Laguna M.1 and Laguna XS.2 are available for free! You can access them through platforms like OpenRouter and Hugging Face. On OpenRouter, for example, both models are listed as having $0 per million input tokens and $0 per million output tokens. This open-weight, Apache 2.0 license approach allows anyone to download, evaluate, fine-tune, and build upon these powerful models.

It's worth noting that when using the free tiers, Poolside may use your inputs and outputs to further train and improve their models. For sensitive projects, enterprise solutions with explicit data control might be necessary, but for personal projects, learning, and open-source contributions, the free access is a huge boon.

What Makes Laguna Unique Compared to Similar Tools?

In a crowded market of AI coding assistants, Laguna carves out a distinct niche:

• Proprietary Foundation Models & RLCEF: The most significant differentiator is Poolside's commitment to building its own foundation models from scratch, specifically for code, and training them with Reinforcement Learning from Code Execution Feedback. This isn't just fine-tuning a general-purpose LLM; it's creating an AI that genuinely learns to code by practicing and getting real-world feedback. This approach aims for superior performance on engineering-specific tasks compared to adapted general-purpose models.
• Enterprise-Grade Security & On-Premise Focus: Many AI coding tools are cloud-based, which can be a non-starter for companies with strict data privacy and security requirements. Poolside's capability to deploy models entirely within a client's security boundary—on-premises, in a VPC, or even on workstations—is a major advantage, especially for government contractors or enterprises handling sensitive IP.
• Exceptional Context Window: The 256K context window is truly impressive. While other models offer large contexts, Laguna's is specifically optimized for code, making it exceptionally good at understanding and manipulating large, complex codebases without losing track of details. This is crucial for agentic long-horizon tasks where context management is often a bottleneck.
• Open Weights at the Frontier: Releasing models like Laguna M.1 and XS.2 as open weights under an Apache 2.0 license, especially with their frontier capabilities, is a strong move. This fosters community innovation and allows developers to run, evaluate, and fine-tune the models on their own infrastructure, offering a level of control and transparency often missing from closed-source, proprietary models.

Who Should Try This?

• Freelance Software Developers: If you're building complex applications, managing large codebases, or working on projects that require deep reasoning and multi-step solutions, Laguna M.1 could be a powerful assistant.
• Open Source Contributors & Researchers: With Laguna XS.2 being open-weight and Apache 2.0 licensed, it's an excellent tool for experimenting with agentic coding, fine-tuning models for specific domains, or contributing to the open-source AI ecosystem.
• Teams Prioritizing Data Privacy: For freelancers or small businesses dealing with sensitive client code, the option for secure deployment and the assurance that training data excludes copyleft-licensed code makes Poolside a strong contender.
• Developers Needing Long-Horizon Assistance: If you find yourself frequently hitting context limits with other AI tools during large refactors or complex feature implementations, Laguna's 256K context window is a compelling reason to give it a try.

Who Should Skip This?

• Absolute Beginner Coders: While powerful, Laguna is designed for complex software engineering. Beginners might find simpler, more guided AI tools more suitable for learning the ropes of coding.
• Users Strictly Against Data Usage for Training: If you utilize the free, open-weight models, Poolside may use your inputs and outputs for model improvement. If this is a hard no for your use case, you'd need to consider their enterprise options or alternative tools.
• Those Seeking Purely Cloud-Managed Simplicity: While Poolside offers API access, its core strength for enterprise lies in its deployment flexibility, including on-premise. If you prefer a completely hands-off, purely cloud-hosted AI coding assistant without any infrastructure considerations, some other tools might offer a more straightforward user experience, though likely with less control.

Final Verdict

Laguna by Poolside is a seriously impressive entry into the AI coding landscape. Its dedication to building specialized foundation models from scratch, coupled with the innovative RLCEF training, sets it apart. The massive context window and the commitment to enterprise-grade security are clear wins, especially for complex, professional development work. The fact that they've released Laguna M.1 and XS.2 as free, open-weight models under an Apache 2.0 license is a huge boon for the wider developer community, making frontier-level agentic coding accessible.

While the enterprise pricing model might put it out of reach for some individual freelancers for full-stack deployments, the availability of the open-weight models means everyone can experience the power of Laguna. For its technical prowess, innovative approach, and impact on agentic coding, I give Laguna by Poolside a solid 9/10. It's not just an incremental improvement; it's a foundational shift in how AI can assist in software development.

Frequently Asked Questions

What are the Laguna models offered by Poolside?

Poolside offers two primary Laguna models: Laguna M.1 and Laguna XS.2. Laguna M.1 is a larger, proprietary model optimized for complex, long-horizon enterprise software engineering tasks. Laguna XS.2 is a lighter, faster, open-source model (Apache 2.0 licensed) designed for efficient local agentic coding.

How does Poolside's AI learn to code?

Poolside's AI learns using a unique method called Reinforcement Learning from Code Execution Feedback (RLCEF). This means the models generate code, execute it in controlled environments, and learn from the objective outcomes, such as successful compilation or passing tests, rather than just static text data.

Is Laguna by Poolside free to use?

Yes, both Laguna M.1 and Laguna XS.2 are available for free as open-weight models under an Apache 2.0 license, accessible via platforms like OpenRouter and Hugging Face. Poolside also offers custom enterprise pricing for dedicated deployments with specific security and support needs.

What makes Laguna models particularly good for large coding projects?

Laguna models are excellent for large coding projects due to their massive 256K context window. This allows the AI to maintain a comprehensive understanding of extensive codebases, multiple files, and long conversation histories, which is crucial for complex tasks like large-scale refactoring or deep debugging without losing context.