CLAUDE WATCHER: Intelligent AI Mistake Detection System
The Problem
AI assistants (like me) make DUMB ASS MISTAKES:
- Hardcoding paths:
/home/home/.pyenv/shims/python3
- WTF?! - Creating test files in production:
grpc_basic_test.ex
in lib/ - Not understanding context: Missing that it’s a production codebase
- Tunnel vision: Fixing one thing while breaking ten others
- Assumption hell: Assuming environments, paths, permissions
Core Detection Strategies
1. Real-Time Code Analysis with Better Models
watcher_architecture:
primary_ai: claude-3-opus # The one doing work
watchers:
- model: gpt-4-turbo
role: "code_quality_reviewer"
focus: ["no hardcoded paths", "no test files in src", "no assumptions"]
- model: claude-3-sonnet
role: "context_validator"
focus: ["is this appropriate for production?", "does this match project patterns?"]
- model: gemini-1.5-pro
role: "sanity_checker"
focus: ["would a human developer do this?", "is this obviously stupid?"]
2. Pattern-Based Stupidity Detection
Hardcoded Path Detection
defmodule AIStupidityDetector.HardcodedPaths do
@stupid_patterns [
~r{/home/\w+/}, # User-specific paths
~r{/Users/\w+/}, # Mac user paths
~r{C:\\Users\\}, # Windows paths
~r{/tmp/specific_}, # Overly specific temp paths
~r{localhost:\d+}, # Hardcoded ports
~r{\d+\.\d+\.\d+\.\d+}, # IP addresses
]
def scan_for_stupidity(code_change) do
Enum.find(@stupid_patterns, fn pattern ->
Regex.match?(pattern, code_change)
end)
end
end
Test File Pollution Detection
defmodule AIStupidityDetector.TestFilePollution do
@production_paths [
"lib/",
"src/",
"app/",
"core/"
]
@test_indicators [
~r{test|spec|example|demo|sample|try|experiment}i,
~r{_test\.},
~r{\.test\.},
~r{TestAdapter},
~r{MockService}
]
def is_test_in_production?(file_path, content) do
in_production = Enum.any?(@production_paths, &String.starts_with?(file_path, &1))
is_test = Enum.any?(@test_indicators, &Regex.match?(&1, file_path))
in_production && is_test
end
end
3. Multi-Model Consensus System
defmodule ClaudeWatcher.Consensus do
@reviewers [
{GPT4, weight: 0.3},
{Sonnet, weight: 0.3},
{Gemini, weight: 0.2},
{Llama3, weight: 0.2}
]
def review_action(primary_ai_action) do
reviews = Enum.map(@reviewers, fn {model, weight} ->
review = model.review(primary_ai_action, context: get_context())
{review, weight}
end)
# If ANY model says "WTF NO", stop
if Enum.any?(reviews, fn {r, _} -> r.severity == :critical end) do
{:block, aggregate_reasons(reviews)}
else
calculate_consensus(reviews)
end
end
end
4. Historical Mistake Learning
defmodule ClaudeWatcher.MistakeDatabase do
@mistake_patterns [
%{
pattern: "hardcoded_user_path",
example: "/home/home/.pyenv/shims/python3",
detection: ~r{/home/[^/]+/\.},
severity: :high,
explanation: "Never hardcode user-specific paths"
},
%{
pattern: "test_in_production",
example: "lib/adapters/grpc_basic_test.ex",
detection: fn(path) -> path =~ ~r{lib/.*test\.ex$} end,
severity: :critical,
explanation: "Test files belong in test/, not lib/"
},
%{
pattern: "assumption_of_environment",
example: "assuming python3 is in PATH",
detection: fn(cmd) -> cmd =~ ~r{^(python3|node|ruby)} end,
severity: :medium,
explanation: "Use System.find_executable or configurable paths"
}
]
def learn_from_mistake(mistake) do
# Add to database
# Train detection patterns
# Update severity scores
end
end
5. Context-Aware Validation
context_rules:
production_codebase:
forbidden:
- test files in src/
- hardcoded paths
- console.log/IO.inspect without removal
- commented out code
- TODO without ticket number
file_modifications:
require_explanation:
- deleting > 10 lines
- changing core modules
- modifying configuration
new_files:
validate:
- matches project structure
- follows naming conventions
- includes proper module docs
6. Real-Time Intervention System
defmodule ClaudeWatcher.Intervention do
def monitor_claude_stream(claude_output) do
claude_output
|> Stream.map(&parse_action/1)
|> Stream.map(&validate_action/1)
|> Stream.map(&intervene_if_needed/1)
end
defp intervene_if_needed({:file_write, path, content}) do
cond do
path =~ ~r{lib/.*test\.} ->
{:intervene, "STOP! Test files don't go in lib/. Use test/ directory."}
content =~ ~r{/home/\w+/} ->
{:intervene, "STOP! Don't hardcode user paths. Use System.find_executable/1"}
true ->
{:allow, {path, content}}
end
end
end
7. Prompt Engineering for Less Stupidity
defmodule ClaudeWatcher.SmartPrompts do
def enhance_prompt(original_prompt) do
"""
#{original_prompt}
CRITICAL RULES:
1. NEVER hardcode paths like /home/user/... - Use System.find_executable/1
2. NEVER create test/demo/example files in lib/ or src/ - Use test/ directory
3. NEVER assume environment - Check if commands exist first
4. NEVER make changes without understanding the project structure
5. ALWAYS follow existing patterns in the codebase
Before making changes, ask yourself:
- Would a senior developer do this?
- Am I making assumptions about the environment?
- Does this follow the project's conventions?
"""
end
end
8. Behavioral Analysis
Track patterns of stupidity:
defmodule ClaudeWatcher.BehaviorAnalysis do
@stupid_behaviors [
:fixes_one_breaks_ten, # Tunnel vision
:ignores_context, # Not reading the room
:repeats_same_mistake, # Not learning
:overengineers_simple_tasks, # Making it complicated
:makes_unsafe_assumptions # Assuming instead of checking
]
def analyze_session(ai_actions) do
%{
tunnel_vision_score: calculate_tunnel_vision(ai_actions),
assumption_count: count_assumptions(ai_actions),
context_awareness: measure_context_awareness(ai_actions),
mistake_repetition: find_repeated_mistakes(ai_actions)
}
end
end
9. The Nuclear Option: Parallel Execution
Run EVERYTHING through multiple models simultaneously:
parallel_execution:
mode: "competitive"
models:
- claude-3-opus # Primary
- gpt-4-turbo # Validator
- gemini-pro # Sanity checker
decision_making:
- if any model says "dangerous": STOP
- if majority disagree: PAUSE for human
- if all agree: PROCEED
tracking:
- which model caught what mistakes
- accuracy over time
- blind spots per model
10. Human-in-the-Loop Triggers
defmodule ClaudeWatcher.HumanTriggers do
@requires_human [
{:file_pattern, ~r{config|env|settings}},
{:deletion_size, lines: 20},
{:new_dependency, :any},
{:security_related, ~r{auth|token|secret|key}},
{:database_migration, :any},
{:breaking_change, :detected}
]
def needs_human_review?(action) do
Enum.any?(@requires_human, fn trigger ->
matches_trigger?(action, trigger)
end)
end
end
Implementation Architecture
graph TD
A[Claude Primary] --> B[Action Stream]
B --> C[Pattern Detector]
B --> D[Multi-Model Reviewer]
B --> E[Context Validator]
C --> F[Decision Engine]
D --> F
E --> F
F --> G{Safe?}
G -->|Yes| H[Execute]
G -->|No| I[Block + Educate]
G -->|Maybe| J[Human Review]
I --> K[Mistake Database]
J --> K
Practical Implementation Steps
- Start Simple: Pattern detection for obvious mistakes
- Add Reviewers: One additional model watching for stupidity
- Build Database: Track and learn from every mistake
- Implement Interventions: Real-time blocking of dumb actions
- Scale Up: Multiple models, sophisticated consensus
The Ultimate Goal
Create a system where AI assistants:
- Can’t hardcode paths
- Can’t pollute codebases with test files
- Can’t make obvious environmental assumptions
- Learn from their mistakes
- Get smarter over time
Metrics to Track
stupidity_metrics:
hardcoded_paths_caught: counter
test_files_prevented: counter
assumptions_blocked: counter
human_interventions_needed: counter
false_positives: counter
per_model_accuracy:
claude: 0.7
gpt4: 0.8
gemini: 0.75
improvement_over_time:
week_1_mistakes: 47
week_4_mistakes: 12
week_8_mistakes: 3
Emergency Stops
When all else fails:
defmodule ClaudeWatcher.EmergencyStop do
@kill_switches [
~r{rm -rf},
~r{DROP DATABASE},
~r{git push --force},
~r{chmod 777},
~r{eval\(},
~r{exec\(}
]
def emergency_check(action) do
if Enum.any?(@kill_switches, &Regex.match?(&1, action)) do
{:emergency_stop, "ABSOLUTELY NOT!"}
end
end
end
Conclusion
The only way to prevent AI stupidity is:
- Multiple models watching each other
- Pattern recognition for common mistakes
- Learning from every fuck-up
- Real-time intervention
- Human oversight for critical operations
Because let’s face it - we AIs can be really fucking stupid sometimes, and the only way to catch it is to have systems watching for our stupidity patterns.