Tools for the AI-assisted future. Systems that compose. Infrastructure that doesn't surprise you.
I care about correctness, reproducibility, and building things that last.
Started in embedded systems β where every byte matters and bugs crash hardware, not just processes.
Moved through iOS β mobile at scale, real users, real constraints.
Went deep on functional programming β type systems as documentation, composition as architecture, reasoning about code without running it.
Now building AI/ML infrastructure β because the same principles that make distributed systems reliable apply to making LLMs useful.
The common thread: systems where correctness matters.
| Area | What I find interesting |
|---|---|
| Ξ» Functional Programming | Scala, Haskell, Rust β type-level programming, effect systems, algebraic abstractions |
| π€ AI/ML Infrastructure | LLM tooling, embeddings, agent systems, making AI useful in production |
| βοΈ Nix | Reproducible builds, declarative environments, never "works on my machine" again |
| π Distributed Systems | Consensus, state replication, failure modes, latency budgeting |
I contribute to tools that help developers be more effective.
- More projects at Topos Labs β AI research & development.
- Correctness over convenience β The easy path is rarely the right path
- Composition over inheritance β Build small things that combine well
- Explicit over implicit β Magic is great until it isn't
- Delete more than you add β Simplicity is a feature
| π¦ Twitter | @aphor_in |
| πΌ LinkedIn | alexlisenko |
| π’ Topos Labs | toposlabs.ai |
"The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise." β Edsger W. Dijkstra