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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).
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.
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.
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.
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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | courses.md | 47 | ## Retrieval and RAG | COMMENT |
| MEDIUM | courses.md | 63 | - 🆕 **[Retrieval Augmented Generation (RAG)](https://www.deeplearning.ai/courses/retrieval-augmented-generation)** 🌐 🎥 ( | CODE |
| MEDIUM⚡ | courses.md | 98 | - **[AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with- | CODE |
| MEDIUM⚡ | courses.md | 100 | - **[Building Agentic RAG with LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamainde | CODE |
| MEDIUM⚡ | courses.md | 103 | - **[ChatGPT and Zapier: Agentic AI for Everyone](https://www.coursera.org/learn/agentic-ai-chatgpt-zapier)** 🌐 📖 by Cou | CODE |
| MEDIUM⚡ | courses.md | 109 | - 🆕 **[Claude Code in Action](https://anthropic.skilljar.com/claude-code-in-action)** 🌐 🎥 (2025) by Anthropic Academy: a | CODE |
| MEDIUM⚡ | courses.md | 111 | - 🆕 **[OpenAI Codex Essentials](https://www.freecodecamp.org/news/openai-codex-essentials-ai-assisted-agentic-developmen | CODE |
| MEDIUM⚡ | courses.md | 120 | - 🆕 **[LLM Evaluations Course](https://www.evidentlyai.com/llm-evaluations-course)** 🌐 🎥 (2025) by Evidently AI: judges, | CODE |
| MEDIUM⚡ | README.md | 28 | | 🏗️ **[Build AI](journeys/build.md)** | [how LLMs work enough to build; the app stack; your first LLM app](journeys/bui | CODE |
| MEDIUM⚡ | README.md | 28 | | 🏗️ **[Build AI](journeys/build.md)** | [how LLMs work enough to build; the app stack; your first LLM app](journeys/bui | CODE |
| MEDIUM⚡ | README.md | 29 | | 🔬 **[Understand AI](journeys/understand.md)** | [transformers; key papers; how to read a paper](journeys/understand.md | CODE |
| MEDIUM | research_updates/ai_evaluation_2025_table.md | 25 | | [AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/abs/2602.06855) | AIRS-Bench | CODE |
| MEDIUM | research_updates/rag_research_table.md | 1 | # :star2: Most Impactful RAG Papers | COMMENT |
| MEDIUM⚡ | research_updates/rag_research_table.md | 24 | | [ChartWalker: Benchmarking the Cross-Chart RAG Task](https://arxiv.org/abs/2606.23997) | This paper introduces ChartWa | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 25 | | [EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory](https://arxiv.org/abs/2606.216 | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 26 | | [Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why](https://arxiv.org/ab | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 35 | | [LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG](https://arxiv.org/abs/2605.06285) | This paper p | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 36 | | [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://arxiv.org/ | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 37 | | [Latent Abstraction for Retrieval-Augmented Generation](https://arxiv.org/abs/2604.17866) | This paper introduces a la | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 38 | | [RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary P | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 39 | | [Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval](https://arxiv | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 40 | | [Doctor-RAG: Failure-Aware Repair for Agentic Retrieval-Augmented Generation](https://arxiv.org/abs/2604.00865) | This | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 41 | | [SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions](http | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 42 | | [A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces](https://arxiv.org/abs/26 | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 49 | | [RAGBoost: Efficient Retrieval-Augmented Generation with Accuracy-Preserving Context Reuse](https://arxiv.org/abs/2511 | CODE |
| MEDIUM⚡ | research_updates/rag_research_table.md | 54 | | [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://arxiv.org/abs/2507.09477) | CODE |
| MEDIUM | research_updates/rag_research_table.md | 78 | |[Agentic Retrieval-Augmented Generation for Time Series Analysis](https://arxiv.org/abs/2408.14484)|This paper introduc | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 28 | | [OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents](https://arxiv.org/abs/2605.05185) | This paper i | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 30 | | [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://arxiv.org/ | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 32 | | [DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data](https://arxiv.org/abs/2604.19859) | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 36 | | [MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification](https://arxiv.org/abs/2603.15726) | This p | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 40 | | [A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces](https://arxiv.org/abs/26 | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 44 | | [Agentic Reasoning for Large Language Models](https://arxiv.org/abs/2601.12538) | This paper explores agentic reasonin | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 45 | | [Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning](https:// | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 53 | | [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://arxiv.org/abs/2506 | CODE |
| MEDIUM⚡ | research_updates/agentic_search_retrieval_table.md | 57 | | [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://arxiv.org/abs/2501.05366) | This paper introduces | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 3 | | 29th June 2026 | [Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent](h | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 4 | | 27th June 2026 | [Agentic Abstention: Do Agents Know When to Stop Instead of Act?](https://arxiv.org/abs/2606.28733) | | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 5 | | 23rd June 2026 | [Qwen-AgentWorld: Language World Models for General Agents](https://arxiv.org/abs/2606.24597) | A wor | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 7 | | 21st June 2026 | [PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 8 | | 16th June 2026 | [LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling](https://arxiv.org/abs/2606 | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 11 | | 13th June 2026 | [Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 14 | | 11th June 2026 | [MiniMax Sparse Attention](https://arxiv.org/abs/2606.13392) | Ultra-long-context capability is becom | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 22 | | 9th June 2026 | [Kwai Keye-VL-2.0 Technical Report](https://arxiv.org/abs/2606.10651) | We introduce Kwai Keye-VL-2.0- | CODE |
| MEDIUM⚡ | research_updates/2026_papers/june_list.md | 25 | | 5th June 2026 | [SWE-Explore: Benchmarking How Coding Agents Explore Repositories](https://arxiv.org/abs/2606.07297) | | CODE |
| MEDIUM⚡ | research_updates/2026_papers/february_list.md | 7 | | 17th February 2026 | [GLM-5: from Vibe Coding to Agentic Engineering](https://arxiv.org/abs/2602.15763) | We present G | CODE |
| MEDIUM⚡ | research_updates/2026_papers/february_list.md | 12 | | 11th February 2026 | [Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters](https://arxiv.org/a | CODE |
| MEDIUM⚡ | research_updates/2026_papers/february_list.md | 20 | | 6th February 2026 | [AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/abs/2602. | CODE |
| MEDIUM⚡ | research_updates/2026_papers/february_list.md | 27 | | 3rd February 2026 | [AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration](https://arxiv.org/abs/2602.0 | CODE |
| MEDIUM⚡ | research_updates/2026_papers/february_list.md | 28 | | 2nd February 2026 | [Kimi K2.5: Visual Agentic Intelligence](https://arxiv.org/abs/2602.02276) | We introduce Kimi K2. | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 3 | | 29th January 2026 | [Scaling Embeddings Outperforms Scaling Experts in Language Models](https://arxiv.org/abs/2601.212 | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 6 | | 26th January 2026 | [daVinci-Dev: Agent-native Mid-training for Software Engineering](https://arxiv.org/abs/2601.18418 | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 7 | | 26th January 2026 | [AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security](https://arxiv.org/ab | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 8 | | 23rd January 2026 | [LongCat-Flash-Thinking-2601 Technical Report](https://arxiv.org/abs/2601.16725) | We introduce Lo | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 10 | | 22nd January 2026 | [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://ar | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 11 | | 22nd January 2026 | [EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience](https://arx | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 12 | | 22nd January 2026 | [LLM-in-Sandbox Elicits General Agentic Intelligence](https://arxiv.org/abs/2601.16206) | We intro | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 14 | | 18th January 2026 | [Agentic Reasoning for Large Language Models](https://arxiv.org/abs/2601.12538) | Reasoning is a f | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 16 | | 14th January 2026 | [DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluati | CODE |
| MEDIUM⚡ | research_updates/2026_papers/january_list.md | 24 | | 11th January 2026 | [MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences](https://arxiv. | CODE |
| 211 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 33 | ### Step 1: Understand Your Evaluation Context | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 44 | ### Step 2: Build Your Reference Dataset | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 63 | ### Step 3: Implement Your Evaluation Metrics | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 89 | ### Step 4: Deploy Smart Log Filtering | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 108 | ### Step 5: Select and Deploy Your Production Metrics | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 126 | ### Step 6: Implement Guardrails and Improvement Loops | COMMENT |
| LOW | …i_evals_for_everyone/chapters/08_evaluation_process.md | 144 | ### Step 7: Build Emerging Issue Discovery | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 48 | ## Step 1: Generate Your Initial Examples | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 75 | ## Step 2: Run Your System on the Examples | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 83 | ## Step 3: Evaluate Alignment with Domain Experts | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 104 | ## Step 4: Identify Error Patterns | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 129 | ## Step 5: Decide Which Metrics You Need | COMMENT |
| LOW | …or_everyone/chapters/04_building_reference_datasets.md | 163 | ## Step 6: Iterate and Expand | COMMENT |
| LOW | …stery_for_everyone/days/day-05-give-it-skills/build.md | 35 | ## Step 1: Inspect a ClawHub Skill | COMMENT |
| LOW | …stery_for_everyone/days/day-05-give-it-skills/build.md | 45 | ## Step 2: Install `document-summary` | COMMENT |
| LOW | …stery_for_everyone/days/day-05-give-it-skills/build.md | 68 | ## Step 3: Create `quick-note` | COMMENT |
| LOW | …stery_for_everyone/days/day-05-give-it-skills/build.md | 93 | ## Step 4: Finalize and Verify | COMMENT |
| LOW | …ry_for_everyone/days/day-03-connect-a-channel/build.md | 20 | ## Step 1: Start the Setup in Web Chat | COMMENT |
| LOW | …stery_for_everyone/days/day-01-install-secure/build.md | 26 | ### Step 4: Run the Security Verification | COMMENT |
| LOW | …stery_for_everyone/days/day-01-install-secure/build.md | 44 | ### Step 5: Name Your Claw | COMMENT |
| LOW | …tery_for_everyone/days/day-10-what-comes-next/build.md | 30 | ## Step 1: Open the Assessment | COMMENT |
| LOW | …tery_for_everyone/days/day-10-what-comes-next/build.md | 40 | ## Step 2: Run the Final Review | COMMENT |
| LOW | …tery_for_everyone/days/day-10-what-comes-next/build.md | 58 | ## Step 3: Paste the Optional Code | COMMENT |
| LOW | …ry_for_everyone/days/day-04-make-it-proactive/build.md | 19 | ## Step 1: Start the Setup in Web Chat | COMMENT |
| LOW | …mastery_for_everyone/days/day-08-let-it-write/build.md | 36 | ## Step 1: Inspect the Send Side of `imap-smtp-email` | COMMENT |
| LOW | …mastery_for_everyone/days/day-08-let-it-write/build.md | 46 | ## Step 2: Configure Outbound Email | COMMENT |
| LOW | …mastery_for_everyone/days/day-08-let-it-write/build.md | 65 | ## Step 3: Create `follow-up-email` | COMMENT |
| LOW | …mastery_for_everyone/days/day-08-let-it-write/build.md | 84 | ## Step 4: Finalize and Verify | COMMENT |
| LOW | …tery_for_everyone/days/day-06-tame-your-inbox/build.md | 38 | ## Step 1: Create a Gmail App Password | COMMENT |
| LOW | …tery_for_everyone/days/day-06-tame-your-inbox/build.md | 53 | ## Step 2: Inspect `imap-smtp-email` | COMMENT |
| LOW | …tery_for_everyone/days/day-06-tame-your-inbox/build.md | 63 | ## Step 3: Install `imap-smtp-email` | COMMENT |
| LOW | …tery_for_everyone/days/day-06-tame-your-inbox/build.md | 81 | ## Step 4: Create `email-triage` | COMMENT |
| LOW | …tery_for_everyone/days/day-06-tame-your-inbox/build.md | 100 | ## Step 5: Finalize and Verify | COMMENT |
| LOW | …stery_for_everyone/days/day-02-give-it-a-soul/build.md | 33 | ## Step 1: Create SOUL.md | COMMENT |
| LOW | …stery_for_everyone/days/day-02-give-it-a-soul/build.md | 56 | ## Step 2: Create USER.md | COMMENT |
| LOW | …stery_for_everyone/days/day-02-give-it-a-soul/build.md | 68 | ## Step 3: Create AGENTS.md | COMMENT |
| LOW | …stery_for_everyone/days/day-02-give-it-a-soul/build.md | 86 | ## Step 4: Create MEMORY.md | COMMENT |
| LOW | …stery_for_everyone/days/day-02-give-it-a-soul/build.md | 103 | ## Step 5: Lock It Down and Verify | COMMENT |
| LOW | …stery_for_everyone/days/day-09-give-it-a-team/build.md | 34 | ## Step 1: Confirm the Writer Model | COMMENT |
| LOW | …stery_for_everyone/days/day-09-give-it-a-team/build.md | 44 | ## Step 2: Create `writer` | COMMENT |
| LOW | …stery_for_everyone/days/day-09-give-it-a-team/build.md | 62 | ## Step 3: Connect the Team | COMMENT |
| LOW | …ery_for_everyone/days/day-07-make-it-research/build.md | 37 | ## Step 1: Get Your Brave Search API Key | COMMENT |
| LOW | …ery_for_everyone/days/day-07-make-it-research/build.md | 51 | ## Step 2: Inspect `web_search` | COMMENT |
| LOW | …ery_for_everyone/days/day-07-make-it-research/build.md | 61 | ## Step 3: Configure `web_search` | COMMENT |
| LOW | …ery_for_everyone/days/day-07-make-it-research/build.md | 76 | ## Step 4: Create `research-brief` | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | research_updates/2024_papers/september_list.md | 30 | | 10th September 2024 | [Can Large Language Models Unlock Novel Scientific Research Ideas?](http://arxiv.org/abs/2409.06 | CODE |
| HIGH⚡ | research_updates/2024_papers/october_list.md | 25 | | 18th October 2024 | [Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts](http: | CODE |
| HIGH⚡ | research_updates/2024_papers/october_list.md | 33 | | 13th October 2024 | [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](http://ar | CODE |
| HIGH | research_updates/2025_papers/january_list.md | 47 | | 31st December 2024 | [MLLM-as-a-Judge for Image Safety without Human Labeling](http://arxiv.org/abs/2501.00192v1) | Im | CODE |
| HIGH | …pplied_LLMs_Mastery_2024/week9_challenges_with_llms.md | 103 | The framework is extensively evaluated across four modern LLMs, revealing a significant prevalence of self-contradiction | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | free_courses/openclaw_mastery_for_everyone/FAQ.md | 50 | 2. **AI API usage**: Depends on your provider and how much you use your Claw. Mid-tier models are significantly cheaper | CODE |
| HIGH | free_courses/openclaw_mastery_for_everyone/README.md | 81 | - 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.md | 11 | If 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 |
| HIGH⚡ | journeys/use.md | 28 | - **[OpenClaw Mastery for Everyone](../free_courses/openclaw_mastery_for_everyone/README.md)** ⭐ 📖 (2026) certified: a 1 | CODE |
| Severity | File | Line | Snippet | Context |
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| MEDIUM | research_updates/state_of_ai_2025_report/index.html | 1699 | <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/h | CODE |
| MEDIUM | research_updates/state_of_ai_2025_report/index.html | 1699 | <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/h | CODE |
| MEDIUM | …e_of_ai_2025_report/sections/03-application-layer.html | 756 | <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/h | CODE |
| MEDIUM | …e_of_ai_2025_report/sections/03-application-layer.html | 756 | <p style="font-size: 0.7rem; color: #888; margin-top: 0.2rem;">Source: <a href="https://github.com/h | CODE |
| MEDIUM | free_courses/ai_evals_for_everyone/slides.html | 3463 | <h3 style="color: #ef4444;">"I need comprehensive coverage from day one"</h3> | CODE |
| Severity | File | Line | Snippet | Context |
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| HIGH | …pplied_LLMs_Mastery_2024/week9_challenges_with_llms.md | 122 | Another mechanism to defend LLMs against adversarial attacks involves the use of a perplexity filter as presented in [th | CODE |
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| LOW | free_courses/ai_evals_for_everyone/slides.html | 1130 | <td class="placeholder">[Generated response]</td> | CODE |
| LOW | free_courses/ai_evals_for_everyone/slides.html | 1135 | <td class="placeholder">[Generated response]</td> | CODE |
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| LOW | research_updates/state_of_ai_2025_report/build.py | 12 | CODE |