Repository Analysis

google/gemma.cpp

lightweight, standalone C++ inference engine for Google's Gemma models.

6.3 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of google/gemma.cpp, a C++ project with 7,007 GitHub stars. SynthScan v2.0 examined 22,033 lines of code across 81 source files, recording 126 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 6.3 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).

6.3
Adjusted Score
6.3
Raw Score
100%
Time Factor
2026-08-02
Last Push
7.0K
Stars
C++
Language
22.0K
Lines of Code
81
Files
126
Pattern Hits
2026-08-02
Scan Date
0.02
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 2MEDIUM 1LOW 123

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 126 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.

Over-Commented Block119 hits · 119 pts
SeverityFileLineSnippetContext
LOWCMakeLists.txt1# Copyright 2019 Google LLCCOMMENT
LOWcmake.sh1#!/usr/bin/env bashCOMMENT
LOWgemma/tensor_info.h1#ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_TENSOR_INFO_H_COMMENT
LOWgemma/tensor_info.h21 std::string base_name;COMMENT
LOWgemma/tensor_info.h41 // The remaining tensors to be concatenated should have just a singleCOMMENT
LOWgemma/attention.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/gemma.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/gemma.h21#include <vector>COMMENT
LOWgemma/weights.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/weights.h21COMMENT
LOWgemma/vit.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/configs.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/configs.h21#include <stddef.h>COMMENT
LOWgemma/tokenizer.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/gemma-inl.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/gemma-inl.h21#include "gemma/activations.h"COMMENT
LOWgemma/gemma_args.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/gemma_args.h21#include <stddef.h>COMMENT
LOWgemma/gemma_args.h81// true to continue generation.COMMENT
LOWgemma/model_store.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/model_store.h21#include <stdint.h>COMMENT
LOWgemma/kv_cache.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/flash_attention.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/activations.h1// Copyright 2024 Google LLCCOMMENT
LOWgemma/bindings/GemmaInterop.cs1// Copyright 2025 Google LLCCOMMENT
LOWgemma/bindings/c_api.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/bindings/c_api.h21#ifdef _WIN32COMMENT
LOWgemma/bindings/context.h1// Copyright 2025 Google LLCCOMMENT
LOWgemma/bindings/context.h21#include <unordered_map>COMMENT
LOWevals/benchmark_helper.h1// Copyright 2024 Google LLCCOMMENT
LOWevals/benchmark_helper.h21#include <string>COMMENT
LOWevals/cross_entropy.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/mat.h1// Copyright 2023 Google LLCCOMMENT
LOWutil/mat.h21#include <stdint.h>COMMENT
LOWutil/threading.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/threading.h21COMMENT
LOWutil/threading.h61 }COMMENT
LOWutil/threading.h81// cores, call sites will have to use nested parallel-for loops as inCOMMENT
LOWutil/threading.h101 // clusters. This is more intuitive than a per-cluster limit for users whoCOMMENT
LOWutil/args.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/allocator.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/allocator.h21#include <stddef.h>COMMENT
LOWutil/threading_context.h1// Copyright 2025 Google LLCCOMMENT
LOWutil/threading_context.h21COMMENT
LOWutil/threading_context.h141 kAcrossClusters,COMMENT
LOWutil/basics.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/basics.h141COMMENT
LOWutil/topology.h1// Copyright 2024 Google LLCCOMMENT
LOWutil/topology.h21COMMENT
LOWutil/test_util.h1// Copyright 2023 Google LLCCOMMENT
LOWpython/run_example.py1# Copyright 2024 Google LLCCOMMENT
LOWpython/convert_from_safetensors.py1# Copyright 2025 Google LLCCOMMENT
LOWpython/convert_from_safetensors.py101# Gemma2 layernorms:COMMENT
LOWio/fields.h1// Copyright 2024 Google LLCCOMMENT
LOWio/fields.h21// https://github.com/libjxl/libjxl, lib/jxl/fields.h.COMMENT
LOWio/fields.h41// and msgpack). This avoids rewriting weights when we add a new field.COMMENT
LOWio/fields.h141// Fields are written in the unchanging order established by the user-definedCOMMENT
LOWio/blob_store.h1// Copyright 2024 Google LLCCOMMENT
LOWio/blob_store.h21#include <stddef.h>COMMENT
LOWio/io.h1// Copyright 2024 Google LLCCOMMENT
59 more matches not shown…
Magic Placeholder Names2 hits · 10 pts
SeverityFileLineSnippetContext
HIGHAPI_SERVER_README.md65export GOOGLE_API_KEY="your-api-key-here"CODE
HIGHAPI_SERVER_README.md69./build/gemma_api_client --api_key "your-api-key" --interactive 1CODE
Structural Annotation Overuse4 hits · 6 pts
SeverityFileLineSnippetContext
LOWREADME.md105### Step 1: Obtain model weights and tokenizer from Kaggle or Hugging Face HubCOMMENT
LOWREADME.md121### Step 2: Extract FilesCOMMENT
LOWREADME.md135### Step 3: BuildCOMMENT
LOWREADME.md202### Step 4: RunCOMMENT
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMutil/threading.h80// barrier synchronization latency than one large pool. However, to utilize allCOMMENT