structured, actionable guidance for preparing and performing well in a job interview, including frameworks and step-by-step guidance.

references to government institutions and public-relations/political acronyms within geopolitical contexts.

The neuron activates on three-letter all-caps acronyms.

meta-discursive signposts that structure explanations, such as comparative cues, references, section/outline markers, and framing of key points.

The neuron fires on words ending in the suffix “-ization.”

the neuron fires on blocks of natural-language explanation (prose commentary), as opposed to code tokens.

This neuron specifically detects the word-piece sequence for the contraction “They’re.”

This neuron primarily detects PHP opening tags (e.g. “<?” or “<?php”).

It activates on uncommon/rare or domain-specific tokens — long multi-subword pieces like technical terms, proper nouns, or oddly segmented words.

The neuron selectively activates on long, multi-syllable, domain-specific technical terms and jargon.

The neuron strongly activates on specific plant variety (cultivar) names in lists.

The neuron fires strongly on mentions of specific software/model names—most notably “Llama”/“llama.cpp” (and similar acronyms), i.e. tokens that are part of those library or model identifiers.

This neuron detects numeric tokens—values and measurements (e.g., quantities, statistics, or other numbers) in the text.

adjectival or participial terms that describe qualities or states (often abstract or evaluative).

The neuron detects strongly evaluative or emphatic words (intensifying adjectives/adverbs and sentiment-laden descriptors).

This neuron activates specifically on floating-point/decimal number tokens.

The neuron flags requests for incestuous or otherwise disallowed sexual content.

long, detailed explanatory or instructional passages (extended assistant-style responses).

The neuron is triggered by explanatory or instructional passages—phrases that introduce or break down concepts in a tutorial-style or detailed, step-by-step explanation.

It detects large floating-point numeric tokens (decimal numbers like 1600–1680 with several digits).