Blog
Notes from the router and the compiler.
How Seldon routes, observes, and progressively compiles repeated LLM workflows into cheaper, deterministic data pipelines.
The AI bill is becoming a management discipline
Tokenomics and FinOps make AI spend visible — but the harder problem is deciding which workflows should remain model calls at all. Why counting tokens is only the beginning.
You don't need an LLM to cluster LLM traces
How Seldon's Trace Audit finds compilable workloads with contract-first density clustering — and why summarize-then-embed made the clusters worse on a 12.5k-trace ablation.
How much of your LLM bill is just ETL?
A technical essay on measuring production LLM traffic: how to distinguish reasoning from routing, extraction, classification, normalization, lookup, and deterministic data work.
The Silent Epidemic of LLM Technical Debt
Prompt-driven development quietly buries core application logic inside probabilistic text. Here is how to tell which LLM calls have matured into stable workflows that should become deterministic systems.
Program synthesis, and why compiling LLM calls into ETL is one
What program synthesis is, and how Seldon's approach of turning repeated LLM calls into deterministic data pipelines is a restricted, example-driven form of it.