AI Slop Code Risk Monitor

HenryNdubuaku/maths-cs-ai-compendium

Name: HenryNdubuaku/maths-cs-ai-compendium — TypeScript Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.

2.1 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for HenryNdubuaku/maths-cs-ai-compendium, a TypeScript project with 7,684 GitHub stars. SynthScan examined 30,999 lines of code across 115 source files, recording 36 pattern matches distributed across 5 syntactic categories. The legacy adjusted pattern signal is 2.1 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

2.1

Adjusted Signal

2.1

Raw Pattern Score

100%

Time Factor

2026-07-18

Last Push

7.7K

Stars

TypeScript

Language

31.0K

Lines of Code

115

Files

Pattern Hits

2026-10-31

Scan Date

0.00

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 7.3/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 25 finding(s).

Hallucinated Dependency & Supply Chain Risk 2.1/100 · LOW

Are imports declared and package/API names verified? 9 finding(s).

Agent Over-Engineering Ratio 1.9/100 · LOW

Are wrappers, guards, and repeated structures necessary? 10 finding(s).

PR Review Velocity Impact 7.2/100 · LOW

Which changes need focused human review? 36 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 5LOW 31

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 36 distinct pattern matches across 5 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Structural Annotation Overuse14 hits · 30 pts

Severity	File	Line	Snippet	Context
LOW⚡	…ogramming/07. vulkan compute and cross-platform GPU.md	639	// Step 1: find max (for numerical stability)	COMMENT
LOW⚡	…ogramming/07. vulkan compute and cross-platform GPU.md	649	// Step 2: compute exp(x - max)	COMMENT
LOW⚡	…ogramming/07. vulkan compute and cross-platform GPU.md	654	// Step 3: sum of exp values	COMMENT
LOW⚡	…ogramming/07. vulkan compute and cross-platform GPU.md	661	// Step 4: normalise	COMMENT
LOW	…apter 16 - SIMD and GPU programming/03. x86 and AVX.md	99	// Step 1: add upper 128 bits to lower 128 bits	COMMENT
LOW	…apter 16 - SIMD and GPU programming/03. x86 and AVX.md	104	// Step 2: horizontal add within 128-bit register	COMMENT
LOW⚡	chapter 17 - AI inference/04. edge inference.md	160	# Step 1: Knowledge distillation (reduce params)	COMMENT
LOW⚡	chapter 17 - AI inference/04. edge inference.md	165	# Step 2: Structured pruning (remove 30% of remaining)	COMMENT
LOW⚡	chapter 17 - AI inference/04. edge inference.md	170	# Step 3: INT4 quantisation	COMMENT
LOW⚡	…10 - multimodal learning/02. vision language models.md	309	# Step 1: Patchify into 4x4 patches -> 4 patches	COMMENT
LOW⚡	…10 - multimodal learning/02. vision language models.md	315	# Step 2: Project patches to embedding dim (d=16)	COMMENT
LOW⚡	…10 - multimodal learning/02. vision language models.md	320	# Step 3: Create text token embeddings (simulate 3 text tokens)	COMMENT
LOW⚡	…10 - multimodal learning/02. vision language models.md	323	# Step 4: Concatenate visual + text tokens	COMMENT
LOW⚡	…10 - multimodal learning/02. vision language models.md	327	# Step 5: Single-head self-attention over the combined sequence	COMMENT

Fake / Example Data9 hits · 14 pts

Severity	File	Line	Snippet	Context
LOW⚡	…puter vision/04. vision transformers and generation.md	240	def d_loss(params, real_data, fake_data):	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	242	fake_score = discriminator(fake_data, params)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	245	def g_loss(params, fake_data):	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	246	fake_score = discriminator(fake_data, params)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	264	fake_data = generator(z, params)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	267	grads = d_grad(params, real_data, fake_data)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	272	fake_data = generator(z, params)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	273	grads = g_grad(params, fake_data)	CODE
LOW⚡	…puter vision/04. vision transformers and generation.md	278	snapshots.append((step, fake_data.copy()))	CODE

Modern AI Meta-Vocabulary3 hits · 9 pts

Severity	File	Line	Snippet	Context
MEDIUM	…putational linguistics/05. advanced text generation.md	389	# RAG-style prompt construction	COMMENT
MEDIUM	…apter 18 - ML systems design/05. ML design examples.md	251	## 6. Conversational AI (RAG-Based Chatbot)	COMMENT
MEDIUM	…apter 18 - ML systems design/05. ML design examples.md	260	### Architecture: Retrieval-Augmented Generation (RAG)	COMMENT

Hyper-Verbose Identifiers8 hits · 8 pts

Severity	File	Line	Snippet	Context
LOW	…IMD and GPU programming/05. triton, TPUs and pallax.md	312	def fused_mul_add_relu_kernel(x_ptr, w_ptr, b_ptr, out_ptr, n, BLOCK: tl.constexpr):	CODE
LOW	…ta structures and algorithms/05. sorting and search.md	351	def longest_common_subsequence(text1, text2):	CODE
LOW	…ta structures and algorithms/01. arrays and hashing.md	333	def length_of_longest_substring(s):	CODE
LOW	chapter 17 - AI inference/01. quantisation.md	85	def quantise_tensor_symmetric(tensor, bits=8):	CODE
LOW	chapter 17 - AI inference/03. serving and batching.md	166	def simulate_continuous_batching(requests, max_batch=8):	CODE
LOW	chapter 06 - machine learning/03. deep learning.md	259	def scaled_dot_product_attention(Q, K, V):	CODE
LOW	…9 - audio and speech/04. speaker and audio analysis.md	304	def generate_verification_pairs(key, n_speakers=20, dim=64, n_pairs=2000):	CODE
LOW	…tware engineering/04. testing and quality assurance.md	103	def test_training_logs_metrics():	CODE

Excessive Try-Catch Wrapping2 hits · 4 pts

Severity	File	Line	Snippet	Context
MEDIUM	chapter 08 - computer vision/05. video and 3D vision.md	275	print(f"Error reduction: {(1 - kalman_error/obs_error) * 100:.1f}%")	CODE
MEDIUM	chapter 04 - statistics/05. inference.md	153	print(f"Error: {abs(pi_estimates[-1] - jnp.pi):.6f}")	CODE