NKTg AI
NKTg AI is not a summarization tool. It is a specialized Language Decoding System that uses physical algorithms to measure semantic energy and extract the Core Content — the core logical genetic code of a text.
- The Nature of Core Content
Core Content is the sentence with the highest density of executable actions and the most concrete outcome in a text. If removed, the text loses key information that cannot be inferred from the rest.
It is an original sentence from the source text — not interpreted or altered by subjective reasoning. 2. Philosophy Generative AI
Reads and reinterprets a text using the AI's own language. NKTg AI
Measures the energy of each word and sentence to identify core content that already exists. Returns the author's original linguistic genetic code without generating new content. 3. Text Structural Genetic Code AMP (Amplifying) + DAMP (Damping) + STABLE = 100% AMP Amplifying > 55%
Increasing energy — Actions, Assertions, Execution, Results. Text has a clear focal point; Core Content has high reliability. DAMP Damping > 55%
Decreasing energy — Conditions, Context, Risks, Exceptions, Counterarguments. Text tends toward condition analysis or risk assessment. STABLE AMP ≈ DAMP
Balanced state — Technical info, Pure data, Data tables, Descriptive content. A prominent Core Content may not exist. 4. Output Modes 🧠 Left Brain (Extraction) — Distillation Standard
Retains the most important sentences by the Golden Ratio. Best for quick reading. Condensed
Removes repetitive or semantically similar sentences. Best for long texts. Essence
Converges to a single Core Content sentence. Recommended when AMP > 55%. 🧠 Right Brain (Addition) — Expansion Refined
Preserves content nucleus with minimum necessary context. Expanded
Core Content combined with surrounding relevant context. Comprehensive (100%)
Full text with DAMPING components marked for easy distinction. 5. Value for Experts & Managers
Quantifiability: AMP%, DAMP%, Compression%, retained sentences
Causality: Core Content often in Action → Result structures
Decision-Making: Quickly evaluate whether to read, examine, or act
Objectivity: Algorithm-based, not subjective emphasis
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Value for General Users
Read Quickly: Identify the highest-action-density sentence instantly Evaluate Before Reading: Observe AMP/DAMP in round 1 Objective Comparison: Compare Core Content across multiple texts Identify Key Points: Spot conditions, warnings, or risks
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Limitations
NKTg AI does not perform well on handwritten documents — OCR accuracy drops significantly compared to printed text, which may affect extraction quality. 8. NKTg Law — Variable Inertia Algorithm NKTg = f(x, v, m) p = m × v NKTg₁ = x × p (Semantic Potential Energy) NKTg₂ = (dm/dt) × p (Semantic Kinetic Energy) NKTg(total) = f(NKTg₁, NKTg₂)
All processing runs directly in the browser via WebAssembly.
Text never leaves the user's device
No server, no cloud, no remote database
History is stored in localStorage — residing entirely on the user's deviceAll rights reserved
