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New developers adopt AI in week 1
AI-powered coding starts early, before durable debugging habits are formed.
Master the Core, Control the Flow
Stop Hiding Behind AI, Keep The Human Edge.
Most new developers adopt AI in their first week. But heavy delegation correlates with weaker debugging depth. Byeori adds intentional friction inside your IDE so you can ship fast and still explain every technical decision under interview pressure.
Join the early cohort to build speed with explainable ownership.
WHY NOW
Senior-looking output can hide junior-level understanding.
AI velocity is no longer the bottleneck. Interview-readiness and technical ownership are.
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Concept and debugging drop
Heavy AI reliance is linked to lower conceptual understanding and independent problem solving.
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Retained understanding
When debugging is fully delegated, only a small fraction of core reasoning is retained.
Source: GitHub Octoverse 2025 + recent learning-retention studies
THREE-PHASE LEARNING SYSTEM
Keep AI speed. Recover human depth.
Byeori operationalizes technical agency with a phased loop inspired by the pitchdeck narrative.
ALIGN
Phase 1 · Strategic Setup
Capture user goals and generate a dynamic roadmap that aligns output speed with long-term skill growth.
You know what to learn before writing another prompt.
PRACTICE
Phase 2 · Intelligent Daily Loop
Pre-coding alignment, active Socratic coding, and post-coding capture run in every development cycle.
Understanding compounds while projects still ship.
PROVE
Phase 3 · Verified Mastery
Competency reporting turns invisible reasoning into explainable ownership for interviews and hiring.
You can explain your architecture, not just present a demo.
BEHAVIOR SHIFT DEMO
From blind patching to evidence-based fixes
This simulation shows how Byeori gates guided fixes until your own hypothesis and rationale are present.
Before
Ask for a patch, apply it quickly, forget the design logic by the next day.
After
State your reasoning first, unlock guidance second, and retain explainable context.
Intentional friction active
interview_guardrail.ts18const hypothesis = await collectUserHypothesis();
19if (!hypothesis) return lockGuidedFix();
20const rationale = await explainFailureContext();
21const guidedFix = await requestGuidedFix(rationale);
22await recordIndependentFixEvent(sessionId);
MEASURABLE OUTCOMES
Track understanding, not just output.
Byeori is positioned where AI-powered velocity and technical agency can coexist.
Independent Fix Rate
How often users resolve issues without immediately delegating the entire fix to AI.
Concept Mastery Score
Short checks confirm whether users can explain root causes and trade-offs.
Query Quality Index
Measure whether prompts become more targeted, contextual, and technically grounded.
Early launch confidence
- Collected from day-one onboarding sessions.
- Works with existing AI coding workflows.
- Designed for interview-defensible project storytelling.
“I built Byeori after facing the same crisis of unexplainable code as a junior builder.”
FAQ
Questions before joining the cohort
Direct answers to the concerns that most often block waitlist conversion.
EARLY ACCESS
Build fast. Explain faster.
Join the waitlist to keep AI velocity while proving your technical depth in real interview scenarios.
No spam. Cohort invites only.