What we did this week: Semantic generator improvements, ChatGPT integration, and “incorrect structures” in Sekura ELI
Over the last seven days the Sekura team advanced the intelligence layer of Sekura ELI — our environment and language for fast, meaning-driven business-logic composition. The work focused on three pillars: a sturdier semantic generator, a more context-aware ChatGPT integration, and an experimental set of simplified developmental patterns we call “incorrect structures” that speed up intuition-building and early semantic bootstrapping.
Semantic generator: more reliable, more human-like
We refactored the semantic generator responsible for turning high-level descriptions into structured models and code fragments. The generator now:
- better extracts context-dependent meanings from noisy descriptions;
- resolves ambiguities in type and structure inference more robustly;
- produces higher-quality outputs for multi-level layouts and property-rich elements;
- is prepared for upcoming LLVM-oriented optimizations.
This work is inspired by how human learners extract statistical regularities from input. Cognitive research on statistical learning shows that the brain detects recurring patterns in streams of information and uses them to build early representations. Sekura’s semantic pipeline mirrors that idea: instead of relying on strict rules, it looks for stable patterns across examples and contexts to propose reliable structures.
Tighter ChatGPT integration: catching intent more reliably
We improved the middleware between Sekura and ChatGPT so ELI can interpret user intent with greater fidelity. The update reduces hallucinations in generated logic, improves how corrections propagate through a design, and leverages the Lazy Merge Context for coherence.
This mirrors a cognitive phenomenon known as fast mapping — the ability to form useful associations after very few exposures. Sekura aims to provide the same “single-shot” usefulness: catch the user’s intent quickly and propose a scaffold that can be refined.
“Incorrect structures”: learning like a child to learn faster
A key experiment this week was introducing a library of intentionally simplified and non-idiomatic patterns we call “incorrect structures.”
Developmental linguistics shows that children naturally use non-standard forms — goed, runned, mouses — as part of rule generalization. These so-called “mistakes” are actually an essential developmental stage, helping the brain consolidate patterns.
Sekura ELI adopts this idea:
- simplified structures help users form an intuitive sense of architecture,
- early interactions require less cognitive effort,
- the transition from idea to production-grade structure becomes smoother.
In other words, “incorrect” forms function as productive stepping stones.
A thin semantic layer: a compact scaffold for English meaning
Sekura ELI constructs a thin semantic core — a compact structure that maps common English phrasing to internal conceptual models. This mirrors modern distributed semantic approaches (LSA, embeddings) where meaning emerges from relationships, not isolated dictionary entries.
The result is smoother reasoning: users rely on a minimal but powerful semantic scaffold to navigate descriptions and logic construction.
Important caveat: Sekura ELI accelerates learning — it does not replace English
We want to emphasize this clearly: Sekura ELI is a catalyst, not a substitute for studying English.
While our semantic layer accelerates early comprehension, long-term proficiency still requires:
- grammar study and vocabulary growth,
- immersion and natural exposure,
- active reading, listening, and speaking practice.
This is fully aligned with research on immersion and implicit learning: structured scaffolding helps at the start, but deep fluency develops through repeated real-world contact.
Practical impact for users
- Faster onboarding and less friction.
- Lower cognitive load when expressing intent.
- More natural ideation flow thanks to smarter ChatGPT interpretation.
- A smoother path from rough idea to workable architecture.