Repository Analysis

donnemartin/system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

10.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of donnemartin/system-design-primer, a Python project with 357,528 GitHub stars. SynthScan v2.0 examined 14,345 lines of code across 52 source files, recording 66 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 10.7 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).

10.7
Adjusted Score
10.7
Raw Score
100%
Time Factor
2026-03-20
Last Push
357.5K
Stars
Python
Language
14.3K
Lines of Code
52
Files
66
Pattern Hits
2026-07-14
Scan Date
0.33
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

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

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 17MEDIUM 0LOW 49

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 66 distinct pattern matches across 4 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.

Cross-File Repetition17 hits · 85 pts
SeverityFileLineSnippetContext
HIGHsolutions/system_design/query_cache/README.md0get the stored query result from the cache. accessing a node updates its position to the front of the lru list.STRING
HIGH…ions/system_design/query_cache/query_cache_snippets.py0get the stored query result from the cache. accessing a node updates its position to the front of the lru list.STRING
HIGHsolutions/object_oriented_design/lru_cache/lru_cache.py0get the stored query result from the cache. accessing a node updates its position to the front of the lru list.STRING
HIGHsolutions/system_design/query_cache/README.md0set the result for the given query key in the cache. when updating an entry, updates its position to the front of the lrSTRING
HIGH…ions/system_design/query_cache/query_cache_snippets.py0set the result for the given query key in the cache. when updating an entry, updates its position to the front of the lrSTRING
HIGHsolutions/object_oriented_design/lru_cache/lru_cache.py0set the result for the given query key in the cache. when updating an entry, updates its position to the front of the lrSTRING
HIGHsolutions/system_design/mint/README.md0return the year and month portions of the timestamp.STRING
HIGHsolutions/system_design/mint/mint_mapreduce.py0return the year and month portions of the timestamp.STRING
HIGHsolutions/system_design/pastebin/pastebin.py0return the year and month portions of the timestamp.STRING
HIGHsolutions/system_design/pastebin/README.md0return the year and month portions of the timestamp.STRING
HIGHsolutions/system_design/pastebin/README-zh-Hans.md0return the year and month portions of the timestamp.STRING
HIGHsolutions/system_design/pastebin/pastebin.py0parse each log line, extract and transform relevant lines. emit key value pairs of the form: (2016-01, url0), 1 (2016-01STRING
HIGHsolutions/system_design/pastebin/README.md0parse each log line, extract and transform relevant lines. emit key value pairs of the form: (2016-01, url0), 1 (2016-01STRING
HIGHsolutions/system_design/pastebin/README-zh-Hans.md0parse each log line, extract and transform relevant lines. emit key value pairs of the form: (2016-01, url0), 1 (2016-01STRING
HIGHsolutions/system_design/pastebin/pastebin.py0sum values for each key. (2016-01, url0), 2 (2016-01, url1), 1STRING
HIGHsolutions/system_design/pastebin/README.md0sum values for each key. (2016-01, url0), 2 (2016-01, url1), 1STRING
HIGHsolutions/system_design/pastebin/README-zh-Hans.md0sum values for each key. (2016-01, url0), 2 (2016-01, url1), 1STRING
Structural Annotation Overuse38 hits · 57 pts
SeverityFileLineSnippetContext
LOWREADME.md226### Step 1: Outline use cases, constraints, and assumptionsCOMMENT
LOWREADME.md239### Step 2: Create a high level designCOMMENT
LOWREADME.md246### Step 3: Design core componentsCOMMENT
LOWREADME.md259### Step 4: Scale the designCOMMENT
LOWREADME.md378### Step 1: Review the scalability video lectureCOMMENT
LOWREADME.md390### Step 2: Review the scalability articleCOMMENT
LOWsolutions/system_design/twitter/README.md7## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/twitter/README.md92## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/twitter/README.md98## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/twitter/README.md222## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/query_cache/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/query_cache/README.md57## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/query_cache/README.md63## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/query_cache/README.md211## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/mint/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/mint/README.md79## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/mint/README.md85## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/mint/README.md326## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/web_crawler/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/web_crawler/README.md68## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/web_crawler/README.md74## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/web_crawler/README.md255## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/pastebin/README.md7## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/pastebin/README.md78## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/pastebin/README.md84## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/pastebin/README.md234## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/sales_rank/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/sales_rank/README.md69## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/sales_rank/README.md75## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/sales_rank/README.md238## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/scaling_aws/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/scaling_aws/README.md63## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/scaling_aws/README.md69## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/scaling_aws/README.md136## Step 4: Scale the designCOMMENT
LOWsolutions/system_design/social_graph/README.md5## Step 1: Outline use cases and constraintsCOMMENT
LOWsolutions/system_design/social_graph/README.md49## Step 2: Create a high level designCOMMENT
LOWsolutions/system_design/social_graph/README.md55## Step 3: Design core componentsCOMMENT
LOWsolutions/system_design/social_graph/README.md249## Step 4: Scale the designCOMMENT
Hyper-Verbose Identifiers10 hits · 10 pts
SeverityFileLineSnippetContext
LOWsolutions/system_design/mint/README.md292 def handle_budget_notifications(self, key, total):CODE
LOWsolutions/system_design/mint/mint_mapreduce.py20 def handle_budget_notifications(self, key, total):CODE
LOWsolutions/system_design/mint/README-zh-Hans.md293 def handle_budget_notifications(self, key, total):CODE
LOW…ions/system_design/web_crawler/web_crawler_snippets.py18 def reduce_priority_link_to_crawl(self, url):CODE
LOW…ions/system_design/web_crawler/web_crawler_snippets.py22 def extract_max_priority_page(self):CODE
LOWsolutions/system_design/web_crawler/README.md118 def reduce_priority_link_to_crawl(self, url)CODE
LOWsolutions/system_design/web_crawler/README.md122 def extract_max_priority_page(self):CODE
LOWsolutions/system_design/web_crawler/README-zh-Hans.md116 def reduce_priority_link_to_crawl(self, url)CODE
LOWsolutions/system_design/web_crawler/README-zh-Hans.md120 def extract_max_priority_page(self):CODE
LOW…ions/object_oriented_design/call_center/call_center.py121 def dispatch_queued_call_to_newly_freed_employee(self, call, employee):CODE
Redundant / Tautological Comments1 hit · 2 pts
SeverityFileLineSnippetContext
LOWgenerate-epub.sh37# Check if dependencies existCOMMENT