Repository Analysis

aishwaryanr/awesome-generative-ai-guide

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

22.7 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of aishwaryanr/awesome-generative-ai-guide, a HTML project with 28,256 GitHub stars. SynthScan v2.0 examined 38,692 lines of code across 196 source files, recording 334 pattern matches distributed across 8 syntactic categories. The overall adjusted score of 22.7 places this repository in the Moderate 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).

22.7
Adjusted Score
22.7
Raw Score
100%
Time Factor
2026-07-14
Last Push
28.3K
Stars
HTML
Language
38.7K
Lines of Code
196
Files
334
Pattern Hits
2026-07-14
Scan Date
0.05
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 10MEDIUM 276LOW 48

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 334 distinct pattern matches across 8 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-Vocabulary271 hits · 738 pts
SeverityFileLineSnippetContext
MEDIUMcourses.md47## Retrieval and RAGCOMMENT
MEDIUMcourses.md63- 🆕 **[Retrieval Augmented Generation (RAG)](https://www.deeplearning.ai/courses/retrieval-augmented-generation)** 🌐 🎥 (CODE
MEDIUMcourses.md98- **[AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-CODE
MEDIUMcourses.md100- **[Building Agentic RAG with LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindeCODE
MEDIUMcourses.md103- **[ChatGPT and Zapier: Agentic AI for Everyone](https://www.coursera.org/learn/agentic-ai-chatgpt-zapier)** 🌐 📖 by CouCODE
MEDIUMcourses.md109- 🆕 **[Claude Code in Action](https://anthropic.skilljar.com/claude-code-in-action)** 🌐 🎥 (2025) by Anthropic Academy: aCODE
MEDIUMcourses.md111- 🆕 **[OpenAI Codex Essentials](https://www.freecodecamp.org/news/openai-codex-essentials-ai-assisted-agentic-developmenCODE
MEDIUMcourses.md120- 🆕 **[LLM Evaluations Course](https://www.evidentlyai.com/llm-evaluations-course)** 🌐 🎥 (2025) by Evidently AI: judges,CODE
MEDIUMREADME.md28| 🏗️ **[Build AI](journeys/build.md)** | [how LLMs work enough to build; the app stack; your first LLM app](journeys/buiCODE
MEDIUMREADME.md28| 🏗️ **[Build AI](journeys/build.md)** | [how LLMs work enough to build; the app stack; your first LLM app](journeys/buiCODE
MEDIUMREADME.md29| 🔬 **[Understand AI](journeys/understand.md)** | [transformers; key papers; how to read a paper](journeys/understand.mdCODE
MEDIUMresearch_updates/ai_evaluation_2025_table.md25| [AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/abs/2602.06855) | AIRS-Bench CODE
MEDIUMresearch_updates/rag_research_table.md1# :star2: Most Impactful RAG PapersCOMMENT
MEDIUMresearch_updates/rag_research_table.md24| [ChartWalker: Benchmarking the Cross-Chart RAG Task](https://arxiv.org/abs/2606.23997) | This paper introduces ChartWaCODE
MEDIUMresearch_updates/rag_research_table.md25| [EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory](https://arxiv.org/abs/2606.216CODE
MEDIUMresearch_updates/rag_research_table.md26| [Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why](https://arxiv.org/abCODE
MEDIUMresearch_updates/rag_research_table.md35| [LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG](https://arxiv.org/abs/2605.06285) | This paper pCODE
MEDIUMresearch_updates/rag_research_table.md36| [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://arxiv.org/CODE
MEDIUMresearch_updates/rag_research_table.md37| [Latent Abstraction for Retrieval-Augmented Generation](https://arxiv.org/abs/2604.17866) | This paper introduces a laCODE
MEDIUMresearch_updates/rag_research_table.md38| [RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary PCODE
MEDIUMresearch_updates/rag_research_table.md39| [Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval](https://arxivCODE
MEDIUMresearch_updates/rag_research_table.md40| [Doctor-RAG: Failure-Aware Repair for Agentic Retrieval-Augmented Generation](https://arxiv.org/abs/2604.00865) | ThisCODE
MEDIUMresearch_updates/rag_research_table.md41| [SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions](httpCODE
MEDIUMresearch_updates/rag_research_table.md42| [A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces](https://arxiv.org/abs/26CODE
MEDIUMresearch_updates/rag_research_table.md49| [RAGBoost: Efficient Retrieval-Augmented Generation with Accuracy-Preserving Context Reuse](https://arxiv.org/abs/2511CODE
MEDIUMresearch_updates/rag_research_table.md54| [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://arxiv.org/abs/2507.09477)CODE
MEDIUMresearch_updates/rag_research_table.md78|[Agentic Retrieval-Augmented Generation for Time Series Analysis](https://arxiv.org/abs/2408.14484)|This paper introducCODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md28| [OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents](https://arxiv.org/abs/2605.05185) | This paper iCODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md30| [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://arxiv.org/CODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md32| [DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data](https://arxiv.org/abs/2604.19859)CODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md36| [MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification](https://arxiv.org/abs/2603.15726) | This pCODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md40| [A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces](https://arxiv.org/abs/26CODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md44| [Agentic Reasoning for Large Language Models](https://arxiv.org/abs/2601.12538) | This paper explores agentic reasoninCODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md45| [Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning](https://CODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md53| [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://arxiv.org/abs/2506CODE
MEDIUMresearch_updates/agentic_search_retrieval_table.md57| [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://arxiv.org/abs/2501.05366) | This paper introduces CODE
MEDIUMresearch_updates/2026_papers/june_list.md3| 29th June 2026 | [Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent](hCODE
MEDIUMresearch_updates/2026_papers/june_list.md4| 27th June 2026 | [Agentic Abstention: Do Agents Know When to Stop Instead of Act?](https://arxiv.org/abs/2606.28733) |CODE
MEDIUMresearch_updates/2026_papers/june_list.md5| 23rd June 2026 | [Qwen-AgentWorld: Language World Models for General Agents](https://arxiv.org/abs/2606.24597) | A worCODE
MEDIUMresearch_updates/2026_papers/june_list.md7| 21st June 2026 | [PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool EcosystemsCODE
MEDIUMresearch_updates/2026_papers/june_list.md8| 16th June 2026 | [LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling](https://arxiv.org/abs/2606CODE
MEDIUMresearch_updates/2026_papers/june_list.md11| 13th June 2026 | [Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-ParameterCODE
MEDIUMresearch_updates/2026_papers/june_list.md14| 11th June 2026 | [MiniMax Sparse Attention](https://arxiv.org/abs/2606.13392) | Ultra-long-context capability is becomCODE
MEDIUMresearch_updates/2026_papers/june_list.md22| 9th June 2026 | [Kwai Keye-VL-2.0 Technical Report](https://arxiv.org/abs/2606.10651) | We introduce Kwai Keye-VL-2.0-CODE
MEDIUMresearch_updates/2026_papers/june_list.md25| 5th June 2026 | [SWE-Explore: Benchmarking How Coding Agents Explore Repositories](https://arxiv.org/abs/2606.07297) |CODE
MEDIUMresearch_updates/2026_papers/february_list.md7| 17th February 2026 | [GLM-5: from Vibe Coding to Agentic Engineering](https://arxiv.org/abs/2602.15763) | We present GCODE
MEDIUMresearch_updates/2026_papers/february_list.md12| 11th February 2026 | [Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters](https://arxiv.org/aCODE
MEDIUMresearch_updates/2026_papers/february_list.md20| 6th February 2026 | [AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/abs/2602.CODE
MEDIUMresearch_updates/2026_papers/february_list.md27| 3rd February 2026 | [AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration](https://arxiv.org/abs/2602.0CODE
MEDIUMresearch_updates/2026_papers/february_list.md28| 2nd February 2026 | [Kimi K2.5: Visual Agentic Intelligence](https://arxiv.org/abs/2602.02276) | We introduce Kimi K2.CODE
MEDIUMresearch_updates/2026_papers/january_list.md3| 29th January 2026 | [Scaling Embeddings Outperforms Scaling Experts in Language Models](https://arxiv.org/abs/2601.212CODE
MEDIUMresearch_updates/2026_papers/january_list.md6| 26th January 2026 | [daVinci-Dev: Agent-native Mid-training for Software Engineering](https://arxiv.org/abs/2601.18418CODE
MEDIUMresearch_updates/2026_papers/january_list.md7| 26th January 2026 | [AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security](https://arxiv.org/abCODE
MEDIUMresearch_updates/2026_papers/january_list.md8| 23rd January 2026 | [LongCat-Flash-Thinking-2601 Technical Report](https://arxiv.org/abs/2601.16725) | We introduce LoCODE
MEDIUMresearch_updates/2026_papers/january_list.md10| 22nd January 2026 | [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://arCODE
MEDIUMresearch_updates/2026_papers/january_list.md11| 22nd January 2026 | [EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience](https://arxCODE
MEDIUMresearch_updates/2026_papers/january_list.md12| 22nd January 2026 | [LLM-in-Sandbox Elicits General Agentic Intelligence](https://arxiv.org/abs/2601.16206) | We introCODE
MEDIUMresearch_updates/2026_papers/january_list.md14| 18th January 2026 | [Agentic Reasoning for Large Language Models](https://arxiv.org/abs/2601.12538) | Reasoning is a fCODE
MEDIUMresearch_updates/2026_papers/january_list.md16| 14th January 2026 | [DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic EvaluatiCODE
MEDIUMresearch_updates/2026_papers/january_list.md24| 11th January 2026 | [MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences](https://arxiv.CODE
211 more matches not shown…
Structural Annotation Overuse45 hits · 68 pts
SeverityFileLineSnippetContext
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md33### Step 1: Understand Your Evaluation ContextCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md44### Step 2: Build Your Reference DatasetCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md63### Step 3: Implement Your Evaluation MetricsCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md89### Step 4: Deploy Smart Log FilteringCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md108### Step 5: Select and Deploy Your Production MetricsCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md126### Step 6: Implement Guardrails and Improvement LoopsCOMMENT
LOW…i_evals_for_everyone/chapters/08_evaluation_process.md144### Step 7: Build Emerging Issue DiscoveryCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md48## Step 1: Generate Your Initial ExamplesCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md75## Step 2: Run Your System on the ExamplesCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md83## Step 3: Evaluate Alignment with Domain ExpertsCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md104## Step 4: Identify Error PatternsCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md129## Step 5: Decide Which Metrics You NeedCOMMENT
LOW…or_everyone/chapters/04_building_reference_datasets.md163## Step 6: Iterate and ExpandCOMMENT
LOW…stery_for_everyone/days/day-05-give-it-skills/build.md35## Step 1: Inspect a ClawHub SkillCOMMENT
LOW…stery_for_everyone/days/day-05-give-it-skills/build.md45## Step 2: Install `document-summary`COMMENT
LOW…stery_for_everyone/days/day-05-give-it-skills/build.md68## Step 3: Create `quick-note`COMMENT
LOW…stery_for_everyone/days/day-05-give-it-skills/build.md93## Step 4: Finalize and VerifyCOMMENT
LOW…ry_for_everyone/days/day-03-connect-a-channel/build.md20## Step 1: Start the Setup in Web ChatCOMMENT
LOW…stery_for_everyone/days/day-01-install-secure/build.md26### Step 4: Run the Security VerificationCOMMENT
LOW…stery_for_everyone/days/day-01-install-secure/build.md44### Step 5: Name Your ClawCOMMENT
LOW…tery_for_everyone/days/day-10-what-comes-next/build.md30## Step 1: Open the AssessmentCOMMENT
LOW…tery_for_everyone/days/day-10-what-comes-next/build.md40## Step 2: Run the Final ReviewCOMMENT
LOW…tery_for_everyone/days/day-10-what-comes-next/build.md58## Step 3: Paste the Optional CodeCOMMENT
LOW…ry_for_everyone/days/day-04-make-it-proactive/build.md19## Step 1: Start the Setup in Web ChatCOMMENT
LOW…mastery_for_everyone/days/day-08-let-it-write/build.md36## Step 1: Inspect the Send Side of `imap-smtp-email`COMMENT
LOW…mastery_for_everyone/days/day-08-let-it-write/build.md46## Step 2: Configure Outbound EmailCOMMENT
LOW…mastery_for_everyone/days/day-08-let-it-write/build.md65## Step 3: Create `follow-up-email`COMMENT
LOW…mastery_for_everyone/days/day-08-let-it-write/build.md84## Step 4: Finalize and VerifyCOMMENT
LOW…tery_for_everyone/days/day-06-tame-your-inbox/build.md38## Step 1: Create a Gmail App PasswordCOMMENT
LOW…tery_for_everyone/days/day-06-tame-your-inbox/build.md53## Step 2: Inspect `imap-smtp-email`COMMENT
LOW…tery_for_everyone/days/day-06-tame-your-inbox/build.md63## Step 3: Install `imap-smtp-email`COMMENT
LOW…tery_for_everyone/days/day-06-tame-your-inbox/build.md81## Step 4: Create `email-triage`COMMENT
LOW…tery_for_everyone/days/day-06-tame-your-inbox/build.md100## Step 5: Finalize and VerifyCOMMENT
LOW…stery_for_everyone/days/day-02-give-it-a-soul/build.md33## Step 1: Create SOUL.mdCOMMENT
LOW…stery_for_everyone/days/day-02-give-it-a-soul/build.md56## Step 2: Create USER.mdCOMMENT
LOW…stery_for_everyone/days/day-02-give-it-a-soul/build.md68## Step 3: Create AGENTS.mdCOMMENT
LOW…stery_for_everyone/days/day-02-give-it-a-soul/build.md86## Step 4: Create MEMORY.mdCOMMENT
LOW…stery_for_everyone/days/day-02-give-it-a-soul/build.md103## Step 5: Lock It Down and VerifyCOMMENT
LOW…stery_for_everyone/days/day-09-give-it-a-team/build.md34## Step 1: Confirm the Writer ModelCOMMENT
LOW…stery_for_everyone/days/day-09-give-it-a-team/build.md44## Step 2: Create `writer`COMMENT
LOW…stery_for_everyone/days/day-09-give-it-a-team/build.md62## Step 3: Connect the TeamCOMMENT
LOW…ery_for_everyone/days/day-07-make-it-research/build.md37## Step 1: Get Your Brave Search API KeyCOMMENT
LOW…ery_for_everyone/days/day-07-make-it-research/build.md51## Step 2: Inspect `web_search`COMMENT
LOW…ery_for_everyone/days/day-07-make-it-research/build.md61## Step 3: Configure `web_search`COMMENT
LOW…ery_for_everyone/days/day-07-make-it-research/build.md76## Step 4: Create `research-brief`COMMENT
Synthetic Comment Markers5 hits · 30 pts
SeverityFileLineSnippetContext
HIGHresearch_updates/2024_papers/september_list.md30| 10th September 2024 | [Can Large Language Models Unlock Novel Scientific Research Ideas?](http://arxiv.org/abs/2409.06CODE
HIGHresearch_updates/2024_papers/october_list.md25| 18th October 2024 | [Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts](http:CODE
HIGHresearch_updates/2024_papers/october_list.md33| 13th October 2024 | [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](http://arCODE
HIGHresearch_updates/2025_papers/january_list.md47| 31st December 2024 | [MLLM-as-a-Judge for Image Safety without Human Labeling](http://arxiv.org/abs/2501.00192v1) | ImCODE
HIGH…pplied_LLMs_Mastery_2024/week9_challenges_with_llms.md103The framework is extensively evaluated across four modern LLMs, revealing a significant prevalence of self-contradictionCODE
Magic Placeholder Names4 hits · 22 pts
SeverityFileLineSnippetContext
HIGHfree_courses/openclaw_mastery_for_everyone/FAQ.md502. **AI API usage**: Depends on your provider and how much you use your Claw. Mid-tier models are significantly cheaper CODE
HIGHfree_courses/openclaw_mastery_for_everyone/README.md81- An API key from your preferred AI provider ([here's how to get one](getting-your-api-key.md))CODE
HIGH…stery_for_everyone/days/day-01-install-secure/build.md11If you don't already have an API key for one of the LLMs, follow [this guide](../../getting-your-api-key.md) to get one.CODE
HIGHjourneys/use.md28- **[OpenClaw Mastery for Everyone](../free_courses/openclaw_mastery_for_everyone/README.md)** ⭐ 📖 (2026) certified: a 1CODE
AI Slop Vocabulary5 hits · 10 pts
SeverityFileLineSnippetContext
MEDIUMresearch_updates/state_of_ai_2025_report/index.html1699 <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/hCODE
MEDIUMresearch_updates/state_of_ai_2025_report/index.html1699 <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/hCODE
MEDIUM…e_of_ai_2025_report/sections/03-application-layer.html756 <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/hCODE
MEDIUM…e_of_ai_2025_report/sections/03-application-layer.html756 <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/hCODE
MEDIUMfree_courses/ai_evals_for_everyone/slides.html3463 <h3 style="color: #ef4444;">"I need comprehensive coverage from day one"</h3>CODE
AI Response Leakage1 hit · 5 pts
SeverityFileLineSnippetContext
HIGH…pplied_LLMs_Mastery_2024/week9_challenges_with_llms.md122Another mechanism to defend LLMs against adversarial attacks involves the use of a perplexity filter as presented in [thCODE
Fake / Example Data2 hits · 2 pts
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LOWfree_courses/ai_evals_for_everyone/slides.html1130 <td class="placeholder">[Generated response]</td>CODE
LOWfree_courses/ai_evals_for_everyone/slides.html1135 <td class="placeholder">[Generated response]</td>CODE
Unused Imports1 hit · 1 pts
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LOWresearch_updates/state_of_ai_2025_report/build.py12CODE