Tolics Engineering Mind

Elixir and OTP offer actors as agents, supervision trees for failure recovery, and process mailboxes as thought logs — a natural runtime for living software.

What would a programming language designed for AI look like? The thought experiment reveals deep unsolved problems and points to probabilistic programming.

Stability contracts, consequence graphs, immutable event logs, and a conductor that turns the gap between intent and reality into autonomous work.

Traditional observability counts individual leaves. In a living system, ambiguity is the vital sign — it reveals where the system is still becoming.

We build AI systems that mimic human workflows. AI-native software should look like a living organism, not a faster factory pipeline.

Sweden's concept of lagom — just the right amount — reveals how collective sufficiency creates societies where empathy becomes natural, not expensive.

Deep focus and extractable attention are fundamentally opposed objectives. The incentive structures and game theory behind why everyone defects toward noise.

Complexity often isn't a bug — it's a feature that benefits interpreters, gatekeepers, and incumbents. A developer's journey from automation idealist to systems realist.

A proof-of-concept tool for graph-based system modelling — defining system behaviour, validating it structurally, and generating tests from the intent.

Practical guide to adopting graph-based system models incrementally. Covers escape hatches, LLM workflows, and week-by-week adoption strategy.

ShellSpark compiles Spark-like Python queries into optimised Unix shell pipelines. 235x faster than Hadoop. On your laptop.

Graph-based system models validate themselves through structural checks, semantic analysis, and automatic test generation from invariants.