Stephen: ColBERT-Style Neural Retrieval for Elixir
ColBERT-style late interaction retrieval for Elixir. Per-token embeddings and MaxSim scoring for more precise search than single-vector approaches.
ColBERT-style late interaction retrieval for Elixir. Per-token embeddings and MaxSim scoring for more precise search than single-vector approaches.
Everyone's an AI-assisted developer now. But the range of what that means is absurd.
Addressing the three most valid criticisms of using Elixir and the BEAM for AI agents.
Test LLM outputs by meaning, not by characters. A wave operator for semantic similarity assertions in Elixir, powered by local embeddings.
Python and JavaScript/TypeScript AI frameworks are reinventing what telecom solved in 1986. What 40 years of production-grade concurrency teaches us about building AI agents.
Or why I really hate this shit...
The classic system design interview opener: design a URL shortener. Each follow-up is supposed to be harder than the last. On the BEAM, they're boring.
My talk at the San Francisco Global Elixir Meetup on building full-stack AI solutions in Elixir: multiple chains, in-memory RAG, MCP clients, and neural networks in your supervision tree.