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Open source software is at the heart of everything we do at Open Tree Technology Lab. From the Kubernetes clusters that run our workloads to the terminal emulators our engineers use every day — our stack is built on open source. Here’s how we approach OSS and why we actively contribute back.

Why We Maintain Forks

If you browse our GitHub organization at github.com/opentreecz, you’ll find forks of projects like Ghostty, Kitty, Alacritty, K3s, OpenWrt, and Chiaki. We don’t fork for the sake of it — each fork serves a purpose:

• Custom patches: Sometimes we need a fix or feature before it lands upstream. Our forks let us move fast while still contributing patches back.
• Specialized builds: Our OpenWrt fork, for example, includes configurations tailored to our network infrastructure.
• Testing and validation: Forks give us a staging ground to test changes before proposing them upstream.

Our Own Tools

Beyond forks, we build and maintain our own tools. openrepo and openrepo-sync are repository management tools written in Python and Rust respectively. They help us keep our Git mirrors synchronized across platforms and automate repository lifecycle management.

Contributing Upstream

We believe that using open source comes with a responsibility to contribute. Bug reports, documentation improvements, and code contributions all flow upstream from our team. It’s not just altruism — it’s good engineering. The healthier the upstream project, the less maintenance burden on our forks.

Building in the Open

Transparency builds trust. By publishing our tools and configurations openly, we invite scrutiny, collaboration, and improvement. It also helps us recruit — talented engineers want to work with teams that contribute to the community.

Check out our repositories at github.com/opentreecz and feel free to open an issue or submit a pull request. We’d love to collaborate.

At Open Tree Technology Lab, automation isn’t just a buzzword — it’s the foundation of how we work. We combine n8n workflow automation with AI-powered tooling to eliminate toil and ship faster.

n8n as Our Automation Backbone

n8n is an open source workflow automation platform that we use extensively. Unlike rigid CI/CD pipelines, n8n lets us build flexible, event-driven workflows that connect our entire toolchain — from Git events to Slack notifications, from monitoring alerts to automated remediation.

Some of our most impactful n8n workflows include:

• Automated incident response: When our monitoring detects an anomaly, n8n triggers a runbook that collects diagnostics, notifies the right team, and can even apply known fixes automatically.
• Release orchestration: Our release pipelines go beyond simple CI/CD. n8n coordinates cross-service deployments, runs smoke tests, and handles rollbacks if something goes wrong.
• Infrastructure drift detection: Scheduled workflows compare the actual state of our infrastructure against our IaC definitions and flag any discrepancies.

Claude Code in Our Engineering Workflow

We’ve integrated Claude Code into our daily development process. It assists with code review, helps write Infrastructure as Code templates, and accelerates debugging. The key is using AI as a force multiplier for our engineers, not a replacement.

Machine Learning in Operations

Beyond conversational AI, we run ML models for predictive scaling, log anomaly detection, and capacity planning. These models are trained on our own operational data and deployed through automated pipelines.

The Result

By combining n8n automation with AI tooling, we’ve reduced our mean time to resolution by over 60% and our deployment frequency has doubled. Automation frees our engineers to focus on architecture and innovation rather than repetitive operational tasks.

If you’re looking to modernize your DevOps practice, start with the repetitive tasks that consume the most engineering time. That’s where automation delivers the highest ROI.

Running your own datacenter in 2026 might sound old-school, but for us at Open Tree Technology Lab, it’s a strategic advantage. Here’s why we believe in hybrid infrastructure.

The Best of Both Worlds

Cloud providers like AWS, GCP, and Azure offer incredible scale and a vast ecosystem of managed services. We use them every day. But there are workloads where owning the metal makes more sense — predictable, long-running services where the economics of cloud compute don’t add up, or cases where data locality and sovereignty matter.

Control and Observability

When you own the hardware, you own the full stack. Our monitoring and observability platform spans both our physical infrastructure and cloud deployments, giving us a unified view of everything. We use the same Infrastructure as Code practices everywhere — Terraform for provisioning, Ansible for configuration, and GitOps for deployment.

Kubernetes Everywhere

Our K3s and Kubernetes clusters run identically whether they’re in our rack or in a cloud region. Containerized microservices don’t care where they run, and that’s the point. We designed our architecture so that workloads can move between environments with minimal friction.

The Cost Equation

For steady-state workloads, owning hardware amortizes quickly. We burst into the cloud for spikes and keep baseline capacity on-prem. This hybrid approach has saved us significant costs while maintaining the flexibility to scale when needed.

If you’re evaluating your infrastructure strategy, consider that it doesn’t have to be all-or-nothing. The best architecture is often the one that uses each platform for what it does best.