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Against Python, syntactically

The same program, side by side:
Sense and Python actually converge quite a bit here — both land on indentation, word-based logic operators, and def/-> for a plain function, because a plain function genuinely is the same concept in both languages: no capability check, no audit entry, nothing Python doesn’t already have. Where they diverge is everything that isn’t a plain function — tool/action/agent/… each keep their own keyword-first shape and returns for a return type, because each one does something Python has no equivalent for (a permission check, a staged commit/ rollback lifecycle, a running entity with its own lifecycle) — plus the scoping rule (Sense picked the less-surprising default over matching Python).

Against “Python + the AI stack”

This is the comparison that actually matters for Sense’s thesis. A conventional AI application needs a language plus a pile of libraries; a Sense program needs the language.

What Sense does not claim to beat Python at

Sense is explicit that it isn’t trying to replace Python for general software — see Non-Goals. No package ecosystem remotely close to PyPI’s, no mature tooling (debugger, profiler, packaging) yet, no bytecode VM (Sense’s reference implementation is a tree-walking interpreter — see Roadmap). It doesn’t need to compete on libraries, though: import python "module" reaches straight into any installed Python package, since Sense’s own interpreter is itself written in Python and runs in the same process.

What Sense claims instead

Not “faster,” not “more libraries.” The claim is narrower and testable: for software whose behavior is partly deterministic and partly probabilistic — coding agents, research agents, enterprise autonomous workers — Sense makes certain mistakes structurally harder to make by accident, because the language itself understands the concepts a plain library can only offer as convention.

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The thesis

The full argument for why this needed to be a language.

Roadmap

What’s actually built today vs. still ahead.