Repository Analysis

AlexsJones/llmfit

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8.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of AlexsJones/llmfit, a Rust project with 29,410 GitHub stars. SynthScan v2.0 examined 67,966 lines of code across 134 source files, recording 271 pattern matches distributed across 12 syntactic categories. The overall adjusted score of 8.7 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).

8.7
Adjusted Score
8.7
Raw Score
100%
Time Factor
2026-07-13
Last Push
29.4K
Stars
Rust
Language
68.0K
Lines of Code
134
Files
271
Pattern Hits
2026-07-14
Scan Date
0.01
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 1MEDIUM 175LOW 95

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 271 distinct pattern matches across 12 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.

Decorative Section Separators172 hits · 496 pts
SeverityFileLineSnippetContext
MEDIUMllmfit-tui/src/tui_ui.rs4885// ── Live inference-bench view ─────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/display.rs488// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/display.rs490// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/display.rs990// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/display.rs992// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/main.rs1754 // ── --status ──────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/main.rs1788 // ── --clear ───────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/main.rs1798 // ── fetch ─────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/main.rs2015// ── bench helpers ──────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/main.rs2727// ── main ───────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs1334 // ── Restore persisted filters ────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs2394 // ── Benchmarks view ──────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3183 // ── Visual mode ──────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3252 // ── Select mode ─────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3318 // ── Quant popup ─────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3352 // ── RunMode popup ───────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3387 // ── Params bucket popup ─────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3422 // ── License popup ───────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3507 // ── Hardware simulation ──────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3661 // ── Advanced Config Popup ──────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs3692 // ── Filter Popup ─────────────────────────────────────────────COMMENT
MEDIUMllmfit-tui/src/tui_app.rs4650 // ── Live inference-bench view ─────────────────────────────────────────COMMENT
MEDIUMscripts/scrape_hf_models.py1301# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/scrape_hf_models.py1303# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/scrape_hf_models.py1067# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/scrape_hf_models.py1069# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py42# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py44# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py69# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py71# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py116# ---------------------------------------------------------------------------COMMENT
MEDIUMscripts/verify_models.py118# ---------------------------------------------------------------------------COMMENT
MEDIUMllmfit-core/src/fit.rs1097// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1099// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1187 // ── Bandwidth-based estimation (preferred) ─────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1404 // ── Fallback: fixed-constant approach ──────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1482// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1484// ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1753 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1755 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1819 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1821 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1892 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs1894 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2089 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2091 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2147 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2149 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2476 // ── Estimate calibration against measured community benchmarks ──────COMMENT
MEDIUMllmfit-core/src/fit.rs2671 // ── Usable context (issue #621) ─────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2671 // ── Usable context (issue #621) ─────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2835 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2837 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2890 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs2892 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs3150 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs3152 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs3425 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs3433 // ────────────────────────────────────────────────────────────────────COMMENT
MEDIUMllmfit-core/src/fit.rs3694 // ────────────────────────────────────────────────────────────────────COMMENT
112 more matches not shown…
Over-Commented Block47 hits · 34 pts
SeverityFileLineSnippetContext
LOWllmfit-tui/src/main.rs41#[derive(clap::ValueEnum, Clone, Copy, Debug)]COMMENT
LOWllmfit-tui/src/main.rs141 #[arg(long)]COMMENT
LOWllmfit-tui/src/main.rs161COMMENT
LOWllmfit-tui/src/main.rs261 Claim {COMMENT
LOWllmfit-tui/src/main.rs541COMMENT
LOWllmfit-tui/src/main.rs601 Download {COMMENT
LOWllmfit-tui/src/main.rs661 /// Number of trending models to fetchCOMMENT
LOWllmfit-tui/src/main.rs761COMMENT
LOWllmfit-tui/src/main.rs821 #[arg(long)]COMMENT
LOWllmfit-tui/src/tui_app.rs361 /// Failed — error message is displayed.COMMENT
LOWllmfit-core/src/fit.rs1use crate::hardware::{GpuBackend, SystemSpecs};COMMENT
LOWllmfit-core/src/fit.rs21 /// Accounts for kernel launch overhead, KV-cache reads, memory controller inefficiency.COMMENT
LOWllmfit-core/src/fit.rs201 /// Speed: estimated tokens/sec normalized to 0-100.COMMENT
LOWllmfit-core/src/fit.rs1101/// Estimate tokens per second for a model on given hardware.COMMENT
LOWllmfit-core/src/fit.rs1121/// - Google, "Efficiently Scaling Transformer Inference" (arXiv:2211.05102)COMMENT
LOWllmfit-core/src/fit.rs1181 .active_parametersCOMMENT
LOWllmfit-core/src/fit.rs1221 //COMMENT
LOWllmfit-core/src/fit.rs1261 gpu_compute_time,COMMENT
LOWllmfit-core/src/fit.rs1281 //COMMENT
LOWllmfit-core/src/fit.rs1301 // - Expert density ratio (more inactive experts → more pressure)COMMENT
LOWllmfit-core/src/fit.rs3501 total_params_b: f64,COMMENT
LOWllmfit-core/src/fit.rs3781 //COMMENT
LOWllmfit-core/src/models.rs501 #[serde(default, skip_serializing_if = "Vec::is_empty")]COMMENT
LOWllmfit-core/src/models.rs521 /// Mamba style layers). When None, all layers are assumed to be fullCOMMENT
LOWllmfit-core/src/models.rs541 pub shared_expert_intermediate_size: Option<u32>,COMMENT
LOWllmfit-core/src/models.rs581#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]COMMENT
LOWllmfit-core/src/models.rs761 /// This is just the model weights: params_b * bytes_per_param.COMMENT
LOWllmfit-core/src/models.rs841 model_mem + kv_cache + overheadCOMMENT
LOWllmfit-core/src/models.rs1101/// Deduplicate a list of [`HfModelEntry`] records by canonical name slug, merging duplicates.COMMENT
LOWllmfit-core/src/models.rs1541COMMENT
LOWllmfit-core/src/update.rs1//! Online model database updates via the HuggingFace Hub API.COMMENT
LOWllmfit-core/src/update.rs401 head_dim: Option<u32>,COMMENT
LOWllmfit-core/src/benchmarks.rs121 #[serde(default)]COMMENT
LOWllmfit-core/src/benchmarks.rs381#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Default)]COMMENT
LOWllmfit-core/src/share.rs1//! Contribute benchmark results back to the project as a GitHub pull request.COMMENT
LOWllmfit-core/src/share.rs41/// requires no client secret, so shipping it in the binary is by design.COMMENT
LOWllmfit-core/src/share.rs501 /// already been submitted upstream and there was nothing to open a PR for.COMMENT
LOWllmfit-core/src/hardware.rs2221 // and should NOT be treated as unified-memory systems.COMMENT
LOWllmfit-core/src/hardware.rs2341COMMENT
LOWllmfit-core/src/hardware.rs2761/// <https://docs.vllm.ai/en/latest/features/quantization/#supported-hardware>COMMENT
LOWllmfit-core/src/providers.rs1021 /// Download a GGUF file from a HuggingFace repository.COMMENT
LOWllmfit-core/src/providers.rs1841// ---------------------------------------------------------------------------COMMENT
LOWllmfit-core/src/providers.rs2401 NoneCOMMENT
LOWllmfit-core/src/providers.rs2601 return None;COMMENT
LOWllmfit-core/src/bench.rs1//! LLM inference benchmarking against Ollama, vLLM, and MLX endpoints.COMMENT
LOWllmfit-core/src/claim.rs1//! Kubernetes DRA claim generation.COMMENT
LOWllmfit-core/src/claim.rs141 serde_json::to_string_pretty(&out).map_err(|e| format!("JSON serialization failed: {e}"))COMMENT
Excessive Try-Catch Wrapping11 hits · 12 pts
SeverityFileLineSnippetContext
MEDIUMscripts/scrape_docker_models.py168 print(f"Error fetching {url}: {e}", file=sys.stderr)CODE
LOWscripts/scrape_hf_models.py388 except Exception as e:CODE
LOWscripts/scrape_hf_models.py721 except Exception:CODE
LOWscripts/scrape_hf_models.py1153 except Exception:CODE
LOWscripts/scrape_hf_models.py1200 except Exception:CODE
LOWscripts/scrape_hf_models.py1562 except Exception as e:CODE
LOWscripts/test_api.py183 except Exception:CODE
LOWscripts/test_api.py263 except Exception as exc:CODE
LOWscripts/test_api.py271 except Exception:CODE
LOWscripts/verify_models.py38 except Exception:CODE
LOWscripts/scrape_benchmarks.py103 except Exception as e:CODE
Deep Nesting12 hits · 11 pts
SeverityFileLineSnippetContext
LOWscripts/validate_generation_scoring.py15CODE
LOWscripts/validate_generation_scoring.py169CODE
LOWscripts/validate_generation_scoring.py204CODE
LOWscripts/validate_generation_scoring.py241CODE
LOWscripts/scrape_hf_models.py826CODE
LOWscripts/scrape_hf_models.py856CODE
LOWscripts/scrape_hf_models.py1159CODE
LOWscripts/scrape_hf_models.py1224CODE
LOWscripts/scrape_hf_models.py1489CODE
LOWscripts/scrape_hf_models.py1644CODE
LOWscripts/scrape_hf_models.py1727CODE
LOWscripts/test_api.py235CODE
Hyper-Verbose Identifiers8 hits · 10 pts
SeverityFileLineSnippetContext
LOWllmfit-python/tests/test_llmfit.py15def test_find_llmfit_bin_raises_when_missing(monkeypatch: pytest.MonkeyPatch) -> None:CODE
LOWscripts/scrape_docker_models.py194def ollama_tag_to_docker_repo(ollama_tag: str) -> str:CODE
LOWscripts/scrape_hf_models.py588def estimate_params_from_arch(config: dict | None) -> int | None:CODE
LOWscripts/scrape_hf_models.py1107def _model_gguf_repo_candidates(repo_id: str) -> list[tuple[str, str]]:CODE
LOWscripts/scrape_hf_models.py1378def _estimate_params_from_config(config: dict) -> int | None:CODE
LOWscripts/test_api.py74def test_models_envelope_and_limit(base_url: str) -> None:CODE
LOWscripts/test_api.py83def test_top_endpoint_excludes_too_tight(base_url: str) -> None:CODE
LOWscripts/test_api.py91def test_filters_runtime_and_use_case(base_url: str) -> None:CODE
Unused Imports7 hits · 7 pts
SeverityFileLineSnippetContext
LOWllmfit-python/hatch_build.py27CODE
LOWllmfit-python/tests/test_llmfit.py3CODE
LOWllmfit-python/src/llmfit/__init__.py1CODE
LOWllmfit-python/src/llmfit/__main__.py1CODE
LOWscripts/validate_generation_scoring.py11CODE
LOWscripts/test_api.py13CODE
LOWscripts/validate_community_benchmarks.py25CODE
Redundant / Tautological Comments4 hits · 6 pts
SeverityFileLineSnippetContext
LOWinstall.sh171 # Check if install dir is in PATHCOMMENT
LOWscripts/update_models.sh27# Check if Python 3 is availableCOMMENT
LOWscripts/update_models.sh75# Check if cargo is availableCOMMENT
LOWscripts/scrape_hf_models.py553 # Check if architecture is in known MoE configsCOMMENT
Modern AI Meta-Vocabulary2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUMscripts/scrape_hf_models.py240 # Embeddings (useful for RAG sizing)COMMENT
MEDIUMscripts/scrape_hf_models.py1310 "feature-extraction", # Embedding models (useful for RAG sizing)CODE
Magic Placeholder Names1 hit · 5 pts
SeverityFileLineSnippetContext
HIGHdocs/providers.md134export LMSTUDIO_API_KEY="your-api-key-here"CODE
Fake / Example Data3 hits · 3 pts
SeverityFileLineSnippetContext
LOWllmfit-desktop/ui/i18n.js252 node.setAttribute('placeholder', t(node.getAttribute('data-i18n-placeholder')));CODE
LOWllmfit-core/data/benchmarks.yaml974 - { pattern: '"company"\\s*:\\s*"Acme Corp"', weight: 1, case_insensitive: true }CODE
LOWllmfit-core/data/benchmarks.yaml976 - { pattern: '"phone"\\s*:\\s*"555-0123"', weight: 1 }CODE
Verbosity Indicators2 hits · 2 pts
SeverityFileLineSnippetContext
LOWllmfit-core/src/fit.rs397 // Step 1: pick the best available execution pathCOMMENT
LOWllmfit-core/src/fit.rs398 // Step 2: score memory fit purely on headroom in that path's memory poolCOMMENT
Structural Annotation Overuse2 hits · 2 pts
SeverityFileLineSnippetContext
LOWllmfit-core/src/fit.rs397 // Step 1: pick the best available execution pathCOMMENT
LOWllmfit-core/src/fit.rs398 // Step 2: score memory fit purely on headroom in that path's memory poolCOMMENT