Repository Analysis

alirezadir/AIMLInterviews

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

6.1 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of alirezadir/AIMLInterviews, a Jupyter Notebook project with 8,671 GitHub stars. SynthScan v2.0 examined 5,006 lines of code across 31 source files, recording 17 pattern matches distributed across 3 syntactic categories. The overall adjusted score of 6.1 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

6.1
Adjusted Score
6.1
Raw Score
100%
Time Factor
2026-07-30
Last Push
8.7K
Stars
Jupyter Notebook
Language
5.0K
Lines of Code
31
Files
17
Pattern Hits
2026-08-02
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 5LOW 12

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 17 distinct pattern matches across 3 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Modern AI Meta-Vocabulary5 hits · 17 pts
SeverityFileLineSnippetContext
MEDIUMsrc/genai-resources.md15### RAG and production systemsCOMMENT
MEDIUMsrc/genai-resources.md21### Agents and agentic systemsCOMMENT
MEDIUMsrc/genai-resources.md23- [Agentic AI Systems](https://www.educative.io/courses/agentic-ai-systems) - Introduces agent architecture and system-dCODE
MEDIUMsrc/genai-resources.md24- [Agentic Design Patterns](https://www.educative.io/courses/agentic-design-patterns) - Covers tools, retrieval, memory,CODE
MEDIUMsrc/genai-resources.md35- [Agentic AI Systems](https://github.com/alirezadir/Agentic-AI-Systems) - System-design notes, implementation examples,CODE
Hyper-Verbose Identifiers11 hits · 12 pts
SeverityFileLineSnippetContext
LOWsrc/MLC/pytorch-ml-coding.md713 def linear_regression_gradients(x, y, weight, bias):CODE
LOWsrc/MLC/solutions/ml_algorithms.py22def cross_entropy_from_logits(logits: np.ndarray, targets: np.ndarray) -> float:CODE
LOWsrc/MLC/solutions/ml_algorithms.py38def linear_regression_gradient_descent(CODE
LOWsrc/MLC/solutions/ml_algorithms.py59def logistic_regression_gradient_descent(CODE
LOWsrc/MLC/solutions/ml_algorithms.py192def principal_component_analysis(CODE
LOWsrc/MLC/solutions/ml_algorithms.py235def scaled_dot_product_attention(CODE
LOWsrc/MLC/solutions/ml_algorithms.py260def binary_classification_metrics(CODE
LOWsrc/MLC/solutions/ml_algorithms.py374def _validate_supervised_inputs(CODE
LOWsrc/MLC/solutions/test_ml_algorithms.py24 def test_softmax_and_cross_entropy_are_stable(self):CODE
LOWsrc/MLC/solutions/test_ml_algorithms.py30 def test_linear_regression_recovers_line(self):CODE
LOWsrc/MLC/solutions/test_ml_algorithms.py37 def test_logistic_regression_separates_simple_data(self):CODE
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWsrc/MLC/solutions/ml_algorithms.py3CODE