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Other literature type . 2026
Data sources: ZENODO
ZENODO
Other literature type . 2026
Data sources: Datacite
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Про неиспользуемые символы в языках программирования

Про неиспользуемые символы в языках программирования

Abstract

Introduction Programming is not just solving problems—it’s a constant battle with the human brain. Developers spend 20–30% of their time not on logic, but on syntax traps: where to put a bracket, how not to mix up variables, how not to forget task order, how not to drown in 300 lines without a hint. Cognitive load piles up—the brain holds at most 5–9 items at once (Miller’s rule), while code demands 15–20. Result: bugs, burnout, lost productivity, especially for beginners. Modern languages (Python, JavaScript, C++, Rust) offer tools for performance—async, lambdas, match-case—but none for the brain. There’s no built-in way to say: “do this first, then that”, “this matters, this is noise”, “roll back five steps”, “split into branches and merge later”. It all stays in your head—and it breaks. We propose a fix: seven universal meta-modifiers—symbols added to the core of any language as native operators. Not a library, not a plugin, not syntactic sugar. A new abstraction layer: symbols act as a “remote control” for the parser, letting humans manage order, priority, time, and branching without extra boilerplate. $ — emphasis, | — word role, ~ — time jump, & — fork, ^ — merge, # — queue, > / 5” without pain. This isn’t about us—it’s about a civilization tired of fragile syntax.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average