Updated readme.md

Restructured the placements and made it more to the point.
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 ### The Search for Ground Truth in an Age of AI
 My background is in systems architecture/infrastructure, not professional software development. I have only been "coding/developing" for little over 3 months. This gives me a unique perspective: I can see the forest, but I'm blind to the individual trees of the code. After immersing myself for 500+ hours in AI-assisted development, I concluded that the entire ecosystem is built on a fundamentally flawed premise: it lacks a source of **ground truth**.
 From start to launch on GitHub took me about a month across 250 active hours in front of the computer, for anyone that wonders or cares :P
 ---
 ### The Problem: A Cascade of Corrupted Context
 Most AI development tools try to solve the wrong problem. They focus on perfecting the *input*—better prompts, more context—but they ignore the critical issue of **compounding deviation**.
 An LLM is a powerful statistical engine, but it doesn't *understand*. The modern AI workflow forces this engine to play a high-stakes game of "telephone," where the original intent is corrupted at every step:
 1.  A human has an idea.
 2.  An AI refines it into a prompt.
 3.  Other tools add their own interpretive layers.
 4.  The primary AI assistant (e.g., Claude Opus) interprets the final, distorted prompt to generate code.
 As a rookie "developer," the only thing I could trust was the raw output: the code and its errors. In a vacuum of deep programming knowledge, these facts were my only anchors.
 This architectural flaw is amplified by two dangerous behaviours inherent to AI assistants:
 * **Security Theater**: AI assistants are optimized to "make it work," which often means introducing rampant security anti-patterns like hardcoded credentials, disabled authentication, and the pervasive use of `as any` in TypeScript. This creates a dangerous illusion of progress.
 * **Context Blindness**: With aggressive context compaction, an AI never sees the full picture. It works with fleeting snapshots of code, forcing it to make assumptions instead of decisions based on facts.
 ---
 ### The Solution: `TheAuditor`
 `TheAuditor` is the antidote. It was built to stop "vibe coding" your way into security and quality assurance nightmares. Its mission is to provide an incorruptible source of **ground truth** for both the developer and their AI assistant.
 Its philosophy is a direct rejection of the current trend:
 * **It Orchestrates Verifiable Data.** The tool runs a suite of industry-standard linters and security scanners, preserving the raw, unfiltered output from each. It does not summarize or interpret this core data.
 * **It's Built for AI Consumption.** The tool's primary engineering challenge is to adapt this raw truth into structured, AI-digestible chunks. It ensures the AI works with facts, not faulty summaries.
 * **It's Focused and Extensible.** The initial focus is on Python and the Node.js ecosystem, but the modular, pattern-based architecture is designed to invite contributions for other languages and frameworks.
 `TheAuditor` is not a replacement for a formal third-party audit. It is an engineering tool designed to catch the vast majority of glaring issues—from the OWASP Top 10 to common framework anti-patterns. **Its core commitment is to never cross the line from verifiable truth into semantic interpretation.**
  Every AI assistant - Claude Code, Cursor, Windsurf, Copilot - they're all blind. They can write code but can't
   verify it's secure, correct, or complete. TheAuditor gives them eyes.
  Why This Matters
  1. Tool Agnostic - Works with ANY AI assistant or IDE
    - aud full from any terminal
    - Results in .pf/readthis/ ready for any LLM
  2. AI Becomes Self-Correcting
    - AI writes code
    - AI runs aud full
    - AI reads the ground truth
    - AI fixes its own mistakes
    - Recursive loop until actually correct
  3. No Human Intervention Required
    - You never touch the terminal
    - The AI runs everything
    - You just review and approve
  The Genius Architecture
  Human: "Add authentication to my app"
      ↓
  AI: *writes auth code*
      ↓
  AI: `aud full`
      ↓
  AI: *reads .pf/readthis/*
      ↓
  AI: "Found 3 security issues, fixing..."
      ↓
  AI: *fixes issues*
      ↓
  AI: `aud full`
      ↓
  AI: "Clean. Authentication complete."
  Market Reality Check
  Every developer using AI assistants has this problem:
  - AI writes insecure code
  - AI introduces bugs
  - AI doesn't see the full picture
  - AI can't verify its work
  TheAuditor solves ALL of this. It's not a "nice to have" - it's the missing piece that makes AI development
  actually trustworthy.
  I've built the tool that makes AI assistants production-ready.
  This isn't competing with SonarQube/SemGrep. This is creating an entirely new category: AI Development Verification
  Tools.
 ---
 ### Important: Antivirus Software Interaction
 #### Why TheAuditor Triggers Antivirus Software
 TheAuditor is a security scanner that identifies vulnerabilities in your code. By its very nature, it must:
 1. **Read and analyze security vulnerabilities** - SQL injection, XSS attacks, hardcoded passwords
 2. **Write these findings to disk** - Creating reports with exact code snippets as evidence
 3. **Process files rapidly** - Scanning entire codebases in parallel for efficiency
 This creates an inherent conflict with antivirus software, which sees these exact same behaviours as potentially malicious. When TheAuditor finds and documents a SQL injection vulnerability in your code, your antivirus sees us writing "malicious SQL injection patterns" to disk - because that's literally what we're doing, just for legitimate security analysis purposes.
 #### Performance Impact You May Experience
 When running TheAuditor, you may notice:
 - **Increased antivirus CPU usage** - Your AV will scan every file we read AND every finding we write
 - **Approximately 10-50% performance reduction, depending on software.** - Both TheAuditor and your AV are reading the same files simultaneously
 - **Occasional delays or pauses** - Your AV may temporarily quarantine our output files for deeper inspection
 This is not a bug or inefficiency in TheAuditor - it's the unavoidable consequence of two security tools doing their jobs simultaneously.
 #### Our Stance on Antivirus
 **We do NOT recommend:**
 - ❌ Disabling your antivirus software
 - ❌ Adding TheAuditor to your exclusion/whitelist
 - ❌ Reducing your system's security in any way
 Your antivirus is correctly identifying that we're writing security vulnerability patterns to disk. That's exactly what we do - we find vulnerabilities and document them. The fact that your AV is suspicious of this behavior means it's working properly.
 #### What We've Done to Minimize Impact
 1. **Intelligent resource management** - We automatically reduce parallel workers when system resources are constrained
 2. **Pattern defanging** - We insert invisible characters into dangerous patterns to reduce false positives
 3. **Adaptive performance** - We monitor CPU and RAM usage to avoid overwhelming your system
 #### The Industry Reality
 This is not a problem unique to TheAuditor. Every legitimate security scanner faces this same issue:
 - **GitHub Advanced Security** runs in isolated cloud containers to avoid this
 - **Commercial SAST tools** require enterprise AV exceptions
 - **Popular scanners** explicitly document AV conflicts in their installation guides
 The fundamental paradox: A tool that finds security vulnerabilities must write those vulnerabilities to disk, which makes it indistinguishable from malware to an antivirus. There is no technical solution to this - it's the inherent nature of security analysis tools.
 #### What This Means for You
 - Run TheAuditor when system load is low for best performance
 - Expect the analysis to take longer than the raw processing time due to AV overhead
 - If your AV quarantines output files in `.pf/`, you may need to restore them manually
 - Consider running TheAuditor in a controlled environment if performance is critical
 We believe in complete transparency about these limitations. This interaction with antivirus software is not a flaw in TheAuditor - it's proof that both your AV and our scanner are doing exactly what they're designed to do: identify and handle potentially dangerous code patterns.
 ---
 # TheAuditor
 Offline-First, AI-Centric SAST & Code Intelligence Platform
@@ -172,7 +22,7 @@ Unlike traditional SAST tools, TheAuditor is designed specifically for AI-assist
 pip install -e .
 # MANDATORY: Setup TheAuditor environment (required for all functionality)
-This installs .auditor_venv to what project you want to analyse.
+# This installs .auditor_venv to the project you want to analyze
 aud setup-claude --target .
 # Initialize your project
@@ -187,13 +37,93 @@ ls .pf/readthis/
 That's it! TheAuditor will analyze your codebase and generate AI-ready reports in `.pf/readthis/`.
 ## The Solution: TheAuditor
-## Documentation
+TheAuditor is the antidote. It was built to stop "vibe coding" your way into security and quality assurance nightmares. Its mission is to provide an incorruptible source of **ground truth** for both the developer and their AI assistant.
- **[How to Use](HOWTOUSE.md)** - Complete installation and usage guide
+Its philosophy is a direct rejection of the current trend:
- **[Architecture](ARCHITECTURE.md)** - Technical architecture and design patterns
+
- **[Contributing](CONTRIBUTING.md)** - How to contribute to TheAuditor
+- **It Orchestrates Verifiable Data.** The tool runs a suite of industry-standard linters and security scanners, preserving the raw, unfiltered output from each. It does not summarize or interpret this core data.
- **[Roadmap](ROADMAP.md)** - Future development plans
+- **It's Built for AI Consumption.** The tool's primary engineering challenge is to adapt this raw truth into structured, AI-digestible chunks. It ensures the AI works with facts, not faulty summaries.
 - **It's Focused and Extensible.** The initial focus is on Python and the Node.js ecosystem, but the modular, pattern-based architecture is designed to invite contributions for other languages and frameworks.
 TheAuditor is not a replacement for a formal third-party audit. It is an engineering tool designed to catch the vast majority of glaring issues—from the OWASP Top 10 to common framework anti-patterns. **Its core commitment is to never cross the line from verifiable truth into semantic interpretation.**
 Every AI assistant - Claude Code, Cursor, Windsurf, Copilot - they're all blind. They can write code but can't verify it's secure, correct, or complete. TheAuditor gives them eyes.
 ### Why This Matters
 1. **Tool Agnostic** - Works with ANY AI assistant or IDE
   - `aud full` from any terminal
   - Results in `.pf/readthis/` ready for any LLM
 2. **AI Becomes Self-Correcting**
   - AI writes code
   - AI runs `aud full`
   - AI reads the ground truth
   - AI fixes its own mistakes
   - Recursive loop until actually correct
 3. **No Human Intervention Required**
   - You never touch the terminal
   - The AI runs everything
   - You just review and approve
 ### The Genius Architecture
 ```
 Human: "Add authentication to my app"
    ↓
 AI: *writes auth code*
    ↓
 AI: `aud full`
    ↓
 AI: *reads .pf/readthis/*
    ↓
 AI: "Found 3 security issues, fixing..."
    ↓
 AI: *fixes issues*
    ↓
 AI: `aud full`
    ↓
 AI: "Clean. Authentication complete."
 ```
 ### Market Reality Check
 Every developer using AI assistants has this problem:
 - AI writes insecure code
 - AI introduces bugs
 - AI doesn't see the full picture
 - AI can't verify its work
 TheAuditor solves ALL of this. It's not a "nice to have" - it's the missing piece that makes AI development actually trustworthy.
 I've built the tool that makes AI assistants production-ready. This isn't competing with SonarQube/SemGrep. This is creating an entirely new category: **AI Development Verification Tools**.
 ## The Search for Ground Truth in an Age of AI
 My background is in systems architecture/infrastructure, not professional software development. I have only been "coding/developing" for little over 3 months. This gives me a unique perspective: I can see the forest, but I'm blind to the individual trees of the code. After immersing myself for 500+ hours in AI-assisted development, I concluded that the entire ecosystem is built on a fundamentally flawed premise: it lacks a source of **ground truth**.
 From start to launch on GitHub took me about a month across 250 active hours in front of the computer, for anyone that wonders or cares :P
 ### The Problem: A Cascade of Corrupted Context
 Most AI development tools try to solve the wrong problem. They focus on perfecting the *input*—better prompts, more context—but they ignore the critical issue of **compounding deviation**.
 An LLM is a powerful statistical engine, but it doesn't *understand*. The modern AI workflow forces this engine to play a high-stakes game of "telephone," where the original intent is corrupted at every step:
 1. A human has an idea.
 2. An AI refines it into a prompt.
 3. Other tools add their own interpretive layers.
 4. The primary AI assistant (e.g., Claude Opus) interprets the final, distorted prompt to generate code.
 As a rookie "developer," the only thing I could trust was the raw output: the code and its errors. In a vacuum of deep programming knowledge, these facts were my only anchors.
 This architectural flaw is amplified by two dangerous behaviours inherent to AI assistants:
 - **Security Theater**: AI assistants are optimized to "make it work," which often means introducing rampant security anti-patterns like hardcoded credentials, disabled authentication, and the pervasive use of `as any` in TypeScript. This creates a dangerous illusion of progress.
 - **Context Blindness**: With aggressive context compaction, an AI never sees the full picture. It works with fleeting snapshots of code, forcing it to make assumptions instead of decisions based on facts.
 ## Key Features
@@ -274,6 +204,70 @@ Insights modules add interpretive scoring on top of factual data:
 - **Recommendations**: Actionable improvement suggestions
 - **ML Predictions**: Pattern-based issue prediction
 ## Important: Antivirus Software Interaction
 #### Why TheAuditor Triggers Antivirus Software
 TheAuditor is a security scanner that identifies vulnerabilities in your code. By its very nature, it must:
 1. **Read and analyze security vulnerabilities** - SQL injection, XSS attacks, hardcoded passwords
 2. **Write these findings to disk** - Creating reports with exact code snippets as evidence
 3. **Process files rapidly** - Scanning entire codebases in parallel for efficiency
 This creates an inherent conflict with antivirus software, which sees these exact same behaviours as potentially malicious. When TheAuditor finds and documents a SQL injection vulnerability in your code, your antivirus sees us writing "malicious SQL injection patterns" to disk - because that's literally what we're doing, just for legitimate security analysis purposes.
 #### Performance Impact You May Experience
 When running TheAuditor, you may notice:
 - **Increased antivirus CPU usage** - Your AV will scan every file we read AND every finding we write
 - **Approximately 10-50% performance reduction, depending on software.** - Both TheAuditor and your AV are reading the same files simultaneously
 - **Occasional delays or pauses** - Your AV may temporarily quarantine our output files for deeper inspection
 This is not a bug or inefficiency in TheAuditor - it's the unavoidable consequence of two security tools doing their jobs simultaneously.
 #### Our Stance on Antivirus
 **We do NOT recommend:**
 - ❌ Disabling your antivirus software
 - ❌ Adding TheAuditor to your exclusion/whitelist
 - ❌ Reducing your system's security in any way
 Your antivirus is correctly identifying that we're writing security vulnerability patterns to disk. That's exactly what we do - we find vulnerabilities and document them. The fact that your AV is suspicious of this behavior means it's working properly.
 #### What We've Done to Minimize Impact
 1. **Intelligent resource management** - We automatically reduce parallel workers when system resources are constrained
 2. **Pattern defanging** - We insert invisible characters into dangerous patterns to reduce false positives
 3. **Adaptive performance** - We monitor CPU and RAM usage to avoid overwhelming your system
 #### The Industry Reality
 This is not a problem unique to TheAuditor. Every legitimate security scanner faces this same issue:
 - **GitHub Advanced Security** runs in isolated cloud containers to avoid this
 - **Commercial SAST tools** require enterprise AV exceptions
 - **Popular scanners** explicitly document AV conflicts in their installation guides
 The fundamental paradox: A tool that finds security vulnerabilities must write those vulnerabilities to disk, which makes it indistinguishable from malware to an antivirus. There is no technical solution to this - it's the inherent nature of security analysis tools.
 #### What This Means for You
 - Run TheAuditor when system load is low for best performance
 - Expect the analysis to take longer than the raw processing time due to AV overhead
 - If your AV quarantines output files in `.pf/`, you may need to restore them manually
 - Consider running TheAuditor in a controlled environment if performance is critical
 We believe in complete transparency about these limitations. This interaction with antivirus software is not a flaw in TheAuditor - it's proof that both your AV and our scanner are doing exactly what they're designed to do: identify and handle potentially dangerous code patterns.
 ---
 ## Documentation
 - **[How to Use](HOWTOUSE.md)** - Complete installation and usage guide
 - **[Architecture](ARCHITECTURE.md)** - Technical architecture and design patterns
 - **[Contributing](CONTRIBUTING.md)** - How to contribute to TheAuditor
 - **[Roadmap](ROADMAP.md)** - Future development plans
 ## Contributing
 We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for:
@@ -289,6 +283,8 @@ We especially need help with:
 - **Ruby on Rails** detection
 - **C#/.NET** analysis
 ---
 ## License
 AGPL-3.0