
Vaskyla Labs
An independent lab for ideas that are not ready to become companies
Vaskyla Labs is an independent research and open-source lab exploring the intersection of machine learning, developer infrastructure, and computational systems. The lab operates deliberately outside the structure of a conventional startup, using small experiments and public software projects to investigate ideas before deciding whether they deserve to become products.
Chapter
Role
Research Lead
Status
Current
Timeline
2023
-
Present
Venture
Founding
Seed
Team Size
2-10
Founded
2023
Website
Why a Lab
Startups create useful pressure.
There is a customer to serve, a product to ship, and a limited amount of time to determine whether an idea deserves more investment. That pressure is valuable, but it also creates a bias toward ideas that can become products quickly.
Some of the most interesting technical questions do not fit that model.
Vaskyla Labs began as a way to explore those questions without immediately turning them into companies.
The lab was intentionally small. There was no large product roadmap and no expectation that every experiment would become commercially useful. We wanted a place where technical ideas could be tested, discarded, rebuilt, and shared without requiring a conventional startup narrative around them.
The first projects focused on machine learning infrastructure and developer tooling. Some were practical experiments. Others were attempts to understand whether a particular technical approach could work at all.
The common thread was curiosity backed by implementation.
We did not want to spend months writing about an idea before knowing whether it worked.
Research Through Building
Vaskyla operates around a simple loop: identify an interesting problem, build the smallest useful experiment, measure what happens, and publish what we learn.
That sounds straightforward, but it creates a very different working environment from a product company.
A project does not need to have a market before it starts. An experiment can fail without becoming a postmortem. A technically promising idea can be abandoned because the economics do not make sense. Conversely, a small internal experiment can become more interesting when someone outside the lab starts using it.
Open source makes that possible.
Whenever practical, we publish the tools, benchmarks, and implementation details behind the work. This creates a feedback loop that is difficult to reproduce internally. Developers can test the software in environments we never anticipated, identify weaknesses we missed, and sometimes take an idea further than the original team could.
The lab benefits from that external pressure.
It also creates a useful discipline: if an idea is worth sharing, we should be able to explain it clearly.
That means documentation is treated as part of the research process rather than an afterthought. A project is not complete simply because the code runs. We want to understand why it works, where it fails, and what assumptions it depends on.


What We Are Exploring
The work at Vaskyla changes over time.
Current experiments span lightweight machine learning systems, tools for understanding complex software environments, and methods for making technical infrastructure easier to inspect and reproduce.
Some projects remain small by design.
Others have attracted contributors and begun developing lives of their own. When that happens, we consider whether the project should remain part of the lab, become an independent open-source effort, or eventually become the foundation for a new company.
That distinction matters.
Not every useful piece of technology needs to become a startup.
There is value in maintaining a place where ideas can exist before they are forced into a business model. Sometimes the right outcome is a company. Sometimes it is a library used by thousands of developers. Sometimes the most valuable result is simply understanding that an approach does not work.
The lab is designed to accommodate all three.
Looking Further
Vaskyla is intentionally small.
The objective is not to build a large organization or maximize the number of projects under development. It is to preserve the conditions that make deep technical work possible: long periods of focused exploration, a high tolerance for failed experiments, strong engineering standards, and enough openness for useful ideas to travel beyond the people who created them.
Over time, some of the lab's work will likely become companies.
Some will disappear.
A few may become infrastructure that quietly sits underneath things other people build.
That is a good outcome.
The purpose of the lab is not to predict which ideas will matter. It is to create enough opportunities for the right ideas to reveal themselves.
