Experimenteren met gerichte synthetische evolutie
Wanneer de theorie de werkelijkheid ontmoet en de code zichzelf begint te ontwikkelen Inspired by thinking about extensions to mostlylucid.mockllmapi and material for the (never to be released but I like to think about it 😜) sci-fi novel "Michael" about emergent AI
**Opmerking:**Opmerking:
Het is ook erg experimenteel, een beetje gek, en zeker "vibe-coded." Je bent gewaarschuwd.
Van theorie naar praktijk:
Ik heb het eigenlijk gebouwd.
Na zes delen van theoretiseren over opkomende intelligentie, multi-agent systemen, wereldwijde consensus, en planetaire-schaal cognitie, had ik een besef:
Ik was aan het uitstellen.**Het is makkelijk om te speculeren over synthetische gilden en evoluerende intelligentie.**Het is moeilijker om het te bouwen.
Dus ik stopte met praten en begon te coderen.
Wat is ontstaan is iets wat ik noem*Gerichte synthetische evolutie (DSE)*Een zelfassemblerende, zelfoptimaliserende workflow met behulp van een multi-level, multi-agent LLM-aangedreven dynamisch systeem.
Of zoiets! (Kijk, ik verzin dit terwijl ik ga.)De lift pitch: Wat als in plaats van het genereren van code een keer en hopen dat het werkt, creëerden we een systeem waar codevoortdurend evolueert
door planning, uitvoering, evaluatie en mutatie?
Wat als we een systeem kunnen leren van zijn fouten, succesvolle patronen kunnen hergebruiken en slimmer kunnen worden na verloop van tijd?Spoiler alarm:. Het werkt eigenlijk wel.*En het is raar.*En fascinerend.
Laten we erin duiken.
You: "Write me a function that does X"
LLM: "Here's some code! [generates 50 lines of Python]"
You: *runs it*
Code: *explodes spectacularly*
You: "Fix it"
LLM: "Oh, sorry! Here's a new version!"
You: *runs it*
Code: *different explosion*
Dit is een EXPERIMENT het is niet zo stabiel en helemaal niet snel.
Maar het doet wat het zegt op het vin,
Echt waar.nu
Doe alle operaties gewoon nog niet WELK.
Het probleem waar we het niet over hebben
Hier is hoe de meeste LLM-gebaseerde code generatie werkt vandaag:
De kwesties zijn van fundamenteel belang:
One-shot-generatie
- Geen iteratie, geen verfijning, geen tweede kansen.
Geen geheugen
Geen kwaliteitsfeedback
[1. PLAN] → [2. GENERATE] → [3. EXECUTE] → [4. EVALUATE] → [5. EVOLVE]
↑ ↓
└────────────────────── [6. LEARN] ←─────────────────────────┘
graph TD
A[User Request] --> B[Overseer LLM<br/>llama3]
B -->|Strategic Plan| C[Generator LLM<br/>codellama]
C -->|Generated Code| D[Executor<br/>Sandboxed Python]
D -->|stdout/stderr/metrics| E[Triage LLM<br/>tinyllama]
E -->|Pass?| F{Quick Check}
F -->|Pass| G[Evaluator LLM<br/>llama3]
F -->|Fail| H[Escalation<br/>qwen2.5-coder]
G -->|Fitness Score| I[RAG Memory]
H -->|Improved Code| D
I -->|Store & Learn| J[Future Reuse]
style B stroke:#e1f5ff,stroke-width:3px
style C stroke:#ffe1f5,stroke-width:3px
style D stroke:#f5ffe1,stroke-width:3px
style E stroke:#fff5e1,stroke-width:3px
style G stroke:#e1ffe1,stroke-width:3px
style I stroke:#f0e1ff,stroke-width:3px
- Dezelfde fouten herhaald eindeloos over soortgelijke problemen
**We hadden iets fundamenteel anders nodig.**Niet alleen betere aanwijzingen.
class OverseerLLM:
"""Plans execution strategies and creates specifications."""
def create_plan(self, task_description: str) -> ExecutionPlan:
"""
Create detailed execution plan from task description.
Returns:
ExecutionPlan with strategy, steps, and expected metrics
"""
# Ask overseer to break down the problem
prompt = f"""Create a detailed execution plan for: {task_description}
Include:
1. High-level strategy
2. Step-by-step implementation plan
3. Expected quality score (0.0-1.0)
4. Expected execution time (ms)
5. Algorithm/data structure choices
6. Edge cases to handle
"""
response = self.client.generate(
model="llama3",
prompt=prompt,
model_key="overseer"
)
return ExecutionPlan(
plan_id=f"plan_{uuid.uuid4().hex[:8]}",
task_description=task_description,
strategy=response,
steps=self._parse_steps(response),
expected_quality=0.8,
expected_speed_ms=1000
)
**Niet alleen grotere modellen.**Een systeem dat echt leert, herinnert en verbetert.
def generate_code(self, specification: str) -> str:
"""Generate code from specification (no creative interpretation)."""
prompt = f"""Implement this specification EXACTLY:
{specification}
Requirements:
- Follow the spec precisely
- No additional features
- Include error handling
- JSON input/output interface
- Return only Python code
"""
code = self.client.generate(
model="codellama",
prompt=prompt,
model_key="generator",
temperature=0.3 # Low temperature for consistency
)
return self._clean_code(code)
**Dat is wat DSE probeert te zijn.**Gerichte synthetische evolutie invoeren
def triage(self, metrics: Dict[str, Any], targets: Dict[str, Any]) -> Dict[str, Any]:
"""Quick triage evaluation using tiny model."""
prompt = f"""Quick evaluation:
Metrics:
- Latency: {metrics['latency_ms']}ms (target: {targets['latency_ms']}ms)
- Memory: {metrics['memory_mb']}MB (target: {targets['memory_mb']}MB)
- Exit code: {metrics['exit_code']} (target: 0)
Does this PASS or FAIL? One word answer."""
response = self.client.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
verdict = "pass" if "pass" in response.lower() else "fail"
return {
"verdict": verdict,
"reason": response.strip(),
"metrics": metrics
}
**Gerichte Synthetische Evolution leent concepten van evolutionaire algoritmen maar past ze toe op code generatie.**Hier is de kern workflow:
def evaluate(self, stdout: str, stderr: str, metrics: Dict) -> Dict[str, Any]:
"""Comprehensive evaluation with multi-dimensional scoring."""
prompt = f"""Evaluate this code execution:
OUTPUT:
{stdout[:500]}
ERRORS:
{stderr[:500] if stderr else "None"}
METRICS:
- Latency: {metrics['latency_ms']}ms
- Memory: {metrics['memory_mb']}MB
- Exit code: {metrics['exit_code']}
Provide scores (0.0-1.0):
1. Correctness: Does output match expected?
2. Quality: Code robustness, patterns, style
3. Speed: Performance vs targets
Format: JSON with correctness, quality, speed, overall_score
"""
response = self.client.evaluate(
code_summary=stdout,
metrics=metrics
)
return {
"correctness": 0.95,
"quality": 0.88,
"speed": 0.92,
"overall_score": 0.92,
"details": response
}
Maar hier wordt het interessant: we gebruiken geen enkele LLM voor alles.
gespecialiseerde agenten**, elk met een specifieke rol:**.
De Multi-Agent Architectuur
User: "Write a fibonacci function"
LLM: [Generates code + tests + documentation + explanation all at once]
[Might invent requirements you didn't ask for]
[Might miss requirements you did ask for]
Agent Verantwoordelijkheden:
User: "Write a fibonacci function"
↓
Overseer: Creates detailed specification
{
"problem": "Generate first N fibonacci numbers",
"algorithm": "Iterative DP approach",
"inputs": {"n": "integer"},
"outputs": {"result": "list[int]"},
"constraints": {
"timeout_ms": 5000,
"max_n": 100
},
"test_cases": [
{"input": {"n": 5}, "expected": [0,1,1,2,3]},
{"input": {"n": 10}, "expected": [0,1,1,2,3,5,8,13,21,34]}
]
}
↓
Generator: Implements ONLY the specification
[No creative interpretation]
[No added features]
[Just clean, focused code]
Overziener (llama3)
Generator (codellama)
nomic-embed-text- Uitgebreide multidimensionale scoressequenceDiagram
participant U as User
participant S as System
participant R as RAG Memory
participant Q as Qdrant DB
participant E as Embedding Model
U->>S: Request: "validate email"
S->>R: Search similar artifacts
R->>E: Generate embedding
E-->>R: 768-dim vector
R->>Q: Semantic search
Q-->>R: Top 5 similar artifacts
R-->>S: Found: email_validator (0.92 similarity)
alt High Similarity (>0.9)
S->>S: Reuse as-is
else Medium Similarity (0.7-0.9)
S->>S: Use as template
else Low Similarity (<0.7)
S->>S: Generate from scratch
end
S->>U: Return solution
S->>R: Store with metadata
R->>E: Generate embedding
E-->>R: Vector
R->>Q: Index artifact
Q-->>R: Stored
Hier is de belangrijkste innovatie die DSE laat werken:
class QdrantRAGMemory:
"""RAG memory using Qdrant vector database for semantic search."""
def __init__(
self,
qdrant_url: str = "http://localhost:6333",
collection_name: str = "code_evolver_artifacts",
embedding_model: str = "nomic-embed-text",
vector_size: int = 768 # nomic-embed-text dimension
):
self.qdrant = QdrantClient(url=qdrant_url)
self.embedding_model = embedding_model
self.vector_size = vector_size
# Create collection if needed
self._init_collection()
def store_artifact(
self,
artifact_id: str,
artifact_type: ArtifactType,
name: str,
content: str,
tags: List[str],
metadata: Dict[str, Any],
auto_embed: bool = True
):
"""Store artifact with semantic embedding."""
# Generate embedding
if auto_embed:
embedding = self._generate_embedding(content)
else:
embedding = None
# Create artifact
artifact = Artifact(
artifact_id=artifact_id,
artifact_type=artifact_type,
name=name,
content=content,
tags=tags,
metadata=metadata
)
# Store in Qdrant with metadata as payload
if embedding:
self.qdrant.upsert(
collection_name=self.collection_name,
points=[
PointStruct(
id=hash(artifact_id) & 0x7FFFFFFF, # Positive int
vector=embedding,
payload={
"artifact_id": artifact_id,
"name": name,
"type": artifact_type.value,
"tags": tags,
"quality_score": metadata.get("quality_score", 0.0),
"latency_ms": metadata.get("latency_ms", 0),
"usage_count": metadata.get("usage_count", 0),
**metadata
}
)
]
)
logger.info(f"✓ Stored artifact '{name}' in RAG memory")
def find_similar(
self,
query: str,
artifact_type: Optional[ArtifactType] = None,
top_k: int = 5,
min_similarity: float = 0.0
) -> List[Tuple[Artifact, float]]:
"""Find similar artifacts using semantic search."""
# Generate query embedding
query_embedding = self._generate_embedding(query)
# Build filter
filter_conditions = []
if artifact_type:
filter_conditions.append(
FieldCondition(
key="type",
match=MatchValue(value=artifact_type.value)
)
)
search_filter = Filter(must=filter_conditions) if filter_conditions else None
# Search Qdrant
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=query_embedding,
query_filter=search_filter,
limit=top_k
)
# Convert to artifacts with similarity scores
artifacts = []
for result in results:
if result.score >= min_similarity:
artifact = self._payload_to_artifact(result.payload)
artifacts.append((artifact, result.score))
return artifacts
def _generate_embedding(self, text: str) -> List[float]:
"""Generate embedding using Ollama."""
response = self.ollama_client.embed(
model=self.embedding_model,
prompt=text
)
return response["embedding"]
Op specificatie gebaseerde generatie
def find_best_tool(
self,
task_description: str,
min_quality: float = 0.7,
max_latency_ms: int = 5000
) -> Optional[Artifact]:
"""Find best tool using multi-dimensional fitness."""
# Search with fitness filters
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=self._generate_embedding(task_description),
query_filter=Filter(
must=[
FieldCondition(
key="type",
match=MatchValue(value="tool")
),
FieldCondition(
key="quality_score",
range=Range(gte=min_quality) # Quality >= 0.7
),
FieldCondition(
key="latency_ms",
range=Range(lte=max_latency_ms) # Latency <= 5000ms
)
]
),
limit=1
)
return results[0] if results else None
Traditionele aanpak (gewend aan hallucinatie):
# Traditional similarity: might give false positives
Task 1: "generate fibonacci sequence"
Task 2: "generate fibonacci backwards"
Similarity: 77% ← High, but these need DIFFERENT code!
# Semantic classification
Triage LLM analyzes both tasks:
SAME → Reuse as-is (just typos/wording differences)
RELATED → Use as template, modify (same domain, different variation)
DIFFERENT → Generate from scratch (completely different problem)
Result: "RELATED - same core algorithm, reversed output"
Action: Load fibonacci code as template, modify to reverse
DSE-aanpak:
RAG-geheugen: leren uit het verleden
(snelheid, kosten, kwaliteit, latentie)
# Original (stored in RAG):
def fibonacci_sequence(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence
# New request: "fibonacci backwards"
# DSE finds original, classifies as RELATED
# Generates modification spec: "Return reversed sequence"
# Modified version:
def fibonacci_backwards(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence[::-1] # ← Only change needed!
Schakel toekomstig hergebruik in
RAG-geheugenimplementatie:
graph LR
A[Tool/Artifact] --> B[Semantic Similarity<br/>0-100]
A --> C[Speed Tier<br/>±20 points]
A --> D[Cost Tier<br/>±15 points]
A --> E[Quality Score<br/>±15 points]
A --> F[Historical Success<br/>±10 points]
A --> G[Latency Metrics<br/>±15 points]
A --> H[Reuse Bonus<br/>±30 points]
B --> I[Final Fitness Score]
C --> I
D --> I
E --> I
F --> I
G --> I
H --> I
I --> J{Selection}
J -->|Highest Score| K[Use This Tool]
style I stroke:#ffeb3b,stroke-width:3px
style K stroke:#4caf50,stroke-width:3px
Filteren op basis van fitness:
def calculate_fitness(tool, similarity_score):
fitness = similarity_score * 100 # Base: 0-100
# Speed tier bonus
if tool.speed_tier == 'very-fast':
fitness += 20
elif tool.speed_tier == 'fast':
fitness += 10
elif tool.speed_tier == 'slow':
fitness -= 10
# Cost tier bonus
if tool.cost_tier == 'free':
fitness += 15
elif tool.cost_tier == 'low':
fitness += 10
elif tool.cost_tier == 'high':
fitness -= 10
# Quality from historical success rate
fitness += tool.quality_score * 10
# Latency metrics
if tool.avg_latency_ms < 100:
fitness += 15 # Very fast
elif tool.avg_latency_ms > 5000:
fitness -= 10 # Too slow
# Reuse bonus
if similarity >= 0.90:
fitness += 30 # Exact match - huge bonus!
elif similarity >= 0.70:
fitness += 15 # Template reuse
return fitness
Hier wordt het slim.**Wanneer je om iets vraagt dat vergelijkbaar is met een vorige taak, meet DSE niet alleen tekst incorrect.Het maakt gebruik van semantische classificatie:**Dit lost het foutpositieve probleem op en maakt intelligent codehergebruik mogelijk.
Als DSE een gerelateerde taak vindt, regenereert het niet van nul.
sequenceDiagram
participant N as Node v1.0.0
participant M as Monitor
participant E as Auto-Evolver
participant O as Overseer
participant G as Generator
participant T as Tester
loop Every Execution
N->>M: Report metrics
M->>M: Track quality history
end
M->>M: Detect degradation
Note over M: Score dropped<br/>0.95 → 0.85<br/>(>15% decline)
M->>E: Trigger evolution
E->>O: Request improvement plan
O-->>E: Strategy: Optimize algorithm
E->>G: Generate v1.1.0
G-->>E: Improved code
E->>T: A/B Test
T->>N: Run v1.0.0
N-->>T: Score: 0.85
T->>E: Run v1.1.0
E-->>T: Score: 0.96
T->>E: v1.1.0 wins!
E->>N: Promote v1.1.0
E->>M: Update lineage
M->>M: Archive v1.0.0
Note over N: Now running v1.1.0<br/>Better performance<br/>Same functionality
In plaats daarvan:
class AutoEvolver:
"""Monitors and evolves code performance automatically."""
def __init__(
self,
performance_threshold: float = 0.15, # 15% degradation triggers evolution
min_runs_before_evolution: int = 3
):
self.performance_threshold = performance_threshold
self.min_runs = min_runs_before_evolution
self.performance_history: Dict[str, List[float]] = {}
def record_execution(self, node_id: str, quality_score: float):
"""Record execution performance."""
if node_id not in self.performance_history:
self.performance_history[node_id] = []
self.performance_history[node_id].append(quality_score)
# Check if evolution needed
if len(self.performance_history[node_id]) >= self.min_runs:
if self._should_evolve(node_id):
self.trigger_evolution(node_id)
def _should_evolve(self, node_id: str) -> bool:
"""Determine if node should evolve based on performance."""
history = self.performance_history[node_id]
if len(history) < self.min_runs:
return False
# Get baseline (best of first 3 runs)
baseline = max(history[:3])
# Get recent average (last 3 runs)
recent_avg = sum(history[-3:]) / 3
# Calculate degradation
degradation = (baseline - recent_avg) / baseline
if degradation > self.performance_threshold:
logger.warning(
f"Node {node_id} degraded {degradation*100:.1f}% "
f"(baseline: {baseline:.2f}, recent: {recent_avg:.2f})"
)
return True
return False
def trigger_evolution(self, node_id: str):
"""Trigger evolution process for underperforming node."""
logger.info(f"Triggering evolution for {node_id}")
# Load current node
node = self.registry.get_node(node_id)
current_code = self.runner.load_code(node_id)
# Get performance metrics
metrics = node.get("metrics", {})
history = self.performance_history[node_id]
# Ask overseer for improvement strategy
improvement_plan = self.overseer.create_improvement_plan(
node_id=node_id,
current_code=current_code,
performance_history=history,
current_metrics=metrics
)
# Generate improved version
new_version = self._increment_version(node.get("version", "1.0.0"))
new_code = self.generator.generate_improvement(
specification=improvement_plan,
base_code=current_code,
version=new_version
)
# A/B test: old vs new
old_score = self._test_version(node_id, current_code)
new_score = self._test_version(f"{node_id}_v{new_version}", new_code)
logger.info(
f"A/B Test Results: "
f"v{node['version']}: {old_score:.2f} | "
f"v{new_version}: {new_score:.2f}"
)
# Keep better version
if new_score > old_score:
logger.info(f"✓ Promoting v{new_version} (improvement: {new_score - old_score:.2f})")
self._promote_version(node_id, new_version, new_code)
else:
logger.info(f"✗ Keeping v{node['version']} (new version worse)")
def _test_version(self, node_id: str, code: str, num_tests: int = 5) -> float:
"""Test a version and return average quality score."""
scores = []
for i in range(num_tests):
stdout, stderr, metrics = self.runner.run_node(node_id, test_input)
result = self.evaluator.evaluate(stdout, stderr, metrics)
scores.append(result.get("overall_score", 0.0))
return sum(scores) / len(scores)
def _promote_version(self, node_id: str, version: str, code: str):
"""Promote new version to production."""
# Archive old version
old_node = self.registry.get_node(node_id)
self.registry.archive_version(node_id, old_node["version"])
# Update node with new version
self.runner.save_code(node_id, code)
self.registry.update_node(node_id, {
"version": version,
"lineage": {
"parent_version": old_node["version"],
"evolution_reason": "performance_degradation",
"timestamp": datetime.utcnow().isoformat()
}
})
# Reset performance tracking
self.performance_history[node_id] = []
logger.info(f"✓ Node {node_id} evolved to v{version}")
Laadt bestaande code
Node: text_processor_v1.0.0
Run 1: Score 0.95 ✓
Run 2: Score 0.94 ✓
Run 3: Score 0.92 ✓
Run 4: Score 0.88 ← Degradation detected!
Run 5: Score 0.85 ← 15% drop, trigger evolution!
Auto-Evolution Process:
1. Analyze performance history
2. Generate improvement specification
3. Create text_processor_v1.1.0
4. A/B test: v1.0.0 vs v1.1.0
5. Keep winner, archive loser
Result: v1.1.0 scores 0.96
Action: Promoted to primary version
als bewezen sjabloon
: "Keep core algoritme, add reversation"
graph TD
A[Complex Task:<br/>Build REST API] --> B[Level 1: Workflow]
B --> C[Design API Schema]
B --> D[Implement Auth]
B --> E[Create Endpoints]
B --> F[Add Error Handling]
B --> G[Write Tests]
C --> C1[Level 2: Nodeplan<br/>Schema validator]
C --> C2[Level 2: Nodeplan<br/>Schema generator]
D --> D1[Level 2: Nodeplan<br/>JWT handler]
D --> D2[Level 2: Nodeplan<br/>User validator]
E --> E1[Level 2: Nodeplan<br/>GET handler]
E --> E2[Level 2: Nodeplan<br/>POST handler]
E --> E3[Level 2: Nodeplan<br/>PUT/DELETE]
C1 --> C1a[Level 3: Function<br/>validate_field]
C1 --> C1b[Level 3: Function<br/>check_types]
D1 --> D1a[Level 3: Function<br/>encode_token]
D1 --> D1b[Level 3: Function<br/>decode_token]
E1 --> E1a[Level 3: Function<br/>parse_params]
E1 --> E1b[Level 3: Function<br/>serialize_response]
style A stroke:#ff6b6b,stroke-width:3px
style B stroke:#4ecdc4,stroke-width:3px
style C stroke:#45b7d1,stroke-width:3px
style D stroke:#45b7d1,stroke-width:3px
style E stroke:#45b7d1,stroke-width:3px
style C1 stroke:#96ceb4,stroke-width:3px
style D1 stroke:#96ceb4,stroke-width:3px
style E1 stroke:#96ceb4,stroke-width:3px
style C1a stroke:#dfe6e9,stroke-width:3px
style D1a stroke:#dfe6e9,stroke-width:3px
style E1a stroke:#dfe6e9,stroke-width:3px
Generator wijzigt sjabloon
class HierarchicalEvolver:
"""Evolves complex workflows through hierarchical decomposition."""
def __init__(
self,
max_depth: int = 3, # Workflow → Nodeplan → Function
max_breadth: int = 5 # Max sub-tasks per level
):
self.max_depth = max_depth
self.max_breadth = max_breadth
def evolve_hierarchical(
self,
root_goal: str,
current_depth: int = 0,
parent_context: Optional[Dict] = None
) -> Dict[str, Any]:
"""
Recursively evolve a complex goal through hierarchical decomposition.
Args:
root_goal: High-level goal description
current_depth: Current depth in hierarchy (0 = workflow level)
parent_context: Context from parent level
Returns:
Evolved workflow with all sub-components
"""
if current_depth >= self.max_depth:
# Base case: generate atomic function
return self._generate_atomic_function(root_goal, parent_context)
# Ask overseer to decompose goal
sub_goals = self.overseer.decompose_goal(
goal=root_goal,
max_sub_goals=self.max_breadth,
context=parent_context
)
logger.info(
f"{' ' * current_depth}Level {current_depth}: "
f"Decomposed '{root_goal}' into {len(sub_goals)} sub-goals"
)
# Evolve each sub-goal recursively
sub_components = []
shared_context = {
"parent_goal": root_goal,
"depth": current_depth,
"sibling_count": len(sub_goals)
}
for i, sub_goal in enumerate(sub_goals):
logger.info(f"{' ' * current_depth}├─ Sub-goal {i+1}/{len(sub_goals)}: {sub_goal}")
# Recursively evolve sub-goal
component = self.evolve_hierarchical(
root_goal=sub_goal,
current_depth=current_depth + 1,
parent_context=shared_context
)
sub_components.append(component)
# Update shared context with learning from this component
shared_context[f"sub_component_{i}_fitness"] = component.get("fitness", 0.0)
# Create workflow/nodeplan from sub-components
workflow = self._assemble_workflow(
goal=root_goal,
sub_components=sub_components,
depth=current_depth
)
return workflow
def _generate_atomic_function(
self,
goal: str,
context: Optional[Dict] = None
) -> Dict[str, Any]:
"""Generate atomic function (leaf node)."""
# Check RAG for similar functions
similar = self.rag.find_similar(
query=goal,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
if similar and similar[0][1] > 0.85:
# High similarity: reuse
logger.info(f" ✓ Reusing similar function: {similar[0][0].name}")
return similar[0][0].to_dict()
# Generate new function
specification = self.overseer.create_plan(
task_description=goal,
context=context
)
code = self.generator.generate_code(specification)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Store in RAG for future reuse
self.rag.store_artifact(
artifact_id=f"func_{hash(goal) & 0x7FFFFFFF}",
artifact_type=ArtifactType.FUNCTION,
name=goal,
content=code,
tags=["hierarchical", f"depth_{context.get('depth', 0)}"],
metadata={
"fitness": evaluation["overall_score"],
"parent_goal": context.get("parent_goal"),
"context": context
},
auto_embed=True
)
return {
"goal": goal,
"code": code,
"fitness": evaluation["overall_score"],
"metrics": metrics
}
def _assemble_workflow(
self,
goal: str,
sub_components: List[Dict],
depth: int
) -> Dict[str, Any]:
"""Assemble workflow from evolved sub-components."""
# Calculate overall fitness (weighted average of sub-components)
total_fitness = sum(c.get("fitness", 0.0) for c in sub_components)
avg_fitness = total_fitness / len(sub_components) if sub_components else 0.0
workflow = {
"goal": goal,
"depth": depth,
"type": "workflow" if depth == 0 else "nodeplan",
"sub_components": sub_components,
"fitness": avg_fitness,
"assembled_at": datetime.utcnow().isoformat()
}
# Store workflow in RAG
workflow_type = ArtifactType.WORKFLOW if depth == 0 else ArtifactType.SUB_WORKFLOW
self.rag.store_artifact(
artifact_id=f"workflow_{hash(goal) & 0x7FFFFFFF}",
artifact_type=workflow_type,
name=goal,
content=json.dumps(workflow, indent=2),
tags=["hierarchical", f"depth_{depth}", f"components_{len(sub_components)}"],
metadata={
"fitness": avg_fitness,
"component_count": len(sub_components),
"depth": depth
},
auto_embed=True
)
logger.info(
f"{' ' * depth}✓ Assembled {workflow['type']}: '{goal}' "
f"(fitness: {avg_fitness:.2f}, components: {len(sub_components)})"
)
return workflow
in plaats van nieuwe code te schrijven
Resultaat
Level 1 (Workflow):
"Build a REST API"
↓
Level 2 (Nodeplans):
├─ Design API schema
├─ Implement authentication
├─ Create CRUD endpoints
├─ Add error handling
└─ Write integration tests
↓
Level 3 (Functions):
Each nodeplan breaks into individual functions
: Sneller, betrouwbaarder, opnieuw getest code
Dit hergebruik versnelt de generatie drastisch en verbetert de betrouwbaarheid.
graph TB
Start([User Request]) --> RAG1[RAG: Search Similar]
RAG1 --> Class{Semantic<br/>Classification}
Class -->|SAME<br/>similarity > 0.9| Reuse[Reuse As-Is]
Class -->|RELATED<br/>0.7-0.9| Template[Template Modification]
Class -->|DIFFERENT<br/>< 0.7| Generate[Generate from Scratch]
Reuse --> Execute
Template --> Overseer1[Overseer: Modification Plan]
Generate --> Overseer2[Overseer: Full Plan]
Overseer1 --> Generator1[Generator: Modify Template]
Overseer2 --> Generator2[Generator: New Code]
Generator1 --> Execute[Execute in Sandbox]
Generator2 --> Execute
Execute --> Triage{Triage<br/>Pass/Fail?}
Triage -->|Fail| Escalate[Escalate to<br/>qwen2.5-coder]
Escalate --> Execute
Triage -->|Pass| Evaluator[Evaluator:<br/>Multi-Dimensional Scoring]
Evaluator --> Fitness[Calculate Fitness Score]
Fitness --> Store[Store in RAG with<br/>Embedding + Metadata]
Store --> Monitor[Performance Monitor]
Monitor --> Degrade{Degradation<br/>Detected?}
Degrade -->|Yes >15%| Evolve[Auto-Evolution:<br/>Generate v1.x.x]
Degrade -->|No| Continue[Continue Monitoring]
Evolve --> ABTest[A/B Test:<br/>Old vs New]
ABTest --> Promote{New Better?}
Promote -->|Yes| Update[Promote New Version]
Promote -->|No| Keep[Keep Old Version]
Update --> Monitor
Keep --> Monitor
Continue --> End([Ready for Reuse])
style Start stroke:#e3f2fd,stroke-width:3px
style RAG1 stroke:#f3e5f5,stroke-width:3px
style Class stroke:#fff3e0,stroke-width:3px
style Reuse stroke:#e8f5e9,stroke-width:3px
style Execute stroke:#fce4ec,stroke-width:3px
style Evaluator stroke:#e1f5fe,stroke-width:3px
style Store stroke:#f1f8e9,stroke-width:3px
style Evolve stroke:#ffe0b2,stroke-width:3px
style End stroke:#e8eaf6,stroke-width:3px
Multi-dimensional fitness: het kiezen van het juiste gereedschap
class DirectedSyntheticEvolution:
"""Complete DSE workflow orchestrator."""
def __init__(self, config: ConfigManager):
self.config = config
self.ollama = OllamaClient(config.ollama_url, config_manager=config)
self.rag = QdrantRAGMemory(
qdrant_url=config.qdrant_url,
ollama_client=self.ollama
)
self.tools = ToolsManager(
ollama_client=self.ollama,
rag_memory=self.rag
)
self.overseer = OverseerLLM(self.ollama, self.rag)
self.generator = CodeGenerator(self.ollama)
self.evaluator = Evaluator(self.ollama)
self.evolver = AutoEvolver(self.rag, self.overseer, self.generator)
def evolve(self, task_description: str) -> Dict[str, Any]:
"""Execute complete evolution workflow."""
logger.info(f"Starting evolution for: {task_description}")
# Step 1: RAG Search for similar solutions
similar = self.rag.find_similar(
query=task_description,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
# Step 2: Semantic Classification
if similar:
relationship = self._classify_relationship(
task_description,
similar[0][0].content,
similar[0][1]
)
else:
relationship = "DIFFERENT"
# Step 3: Choose generation strategy
if relationship == "SAME":
logger.info("✓ Exact match found - reusing as-is")
return similar[0][0].to_dict()
elif relationship == "RELATED":
logger.info("✓ Similar solution found - using as template")
plan = self.overseer.create_modification_plan(
task_description=task_description,
template_code=similar[0][0].content
)
code = self.generator.modify_template(plan, similar[0][0].content)
else: # DIFFERENT
logger.info("✓ No match - generating from scratch")
plan = self.overseer.create_plan(task_description)
code = self.generator.generate_code(plan)
# Step 4: Execute in sandbox
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 5: Triage (quick check)
triage_result = self.evaluator.triage(metrics, targets={})
if triage_result["verdict"] == "fail":
# Escalate to better model
logger.warning("✗ Triage failed - escalating")
code = self._escalate(code, stderr, metrics)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 6: Comprehensive evaluation
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Step 7: Calculate fitness
fitness = self._calculate_fitness(evaluation, metrics)
# Step 8: Store in RAG
artifact_id = f"func_{hash(task_description) & 0x7FFFFFFF}"
self.rag.store_artifact(
artifact_id=artifact_id,
artifact_type=ArtifactType.FUNCTION,
name=task_description,
content=code,
tags=["evolved", "validated"],
metadata={
"quality_score": evaluation["overall_score"],
"latency_ms": metrics["latency_ms"],
"memory_mb": metrics["memory_mb"],
"fitness": fitness,
"relationship": relationship
},
auto_embed=True
)
logger.info(f"✓ Evolution complete - Fitness: {fitness:.2f}")
# Step 9: Start monitoring for future evolution
self.evolver.monitor(artifact_id, evaluation["overall_score"])
return {
"artifact_id": artifact_id,
"code": code,
"fitness": fitness,
"evaluation": evaluation,
"metrics": metrics,
"relationship": relationship
}
def _classify_relationship(
self,
new_task: str,
existing_task: str,
similarity: float
) -> str:
"""Use triage LLM to classify task relationship."""
if similarity < 0.7:
return "DIFFERENT"
prompt = f"""Compare these two tasks:
Task 1 (Existing): {existing_task}
Task 2 (Requested): {new_task}
Similarity Score: {similarity:.2f}
Classify relationship:
- SAME: Minor wording differences, same algorithm
- RELATED: Same domain, different variation
- DIFFERENT: Completely different problems
Answer with one word: SAME, RELATED, or DIFFERENT"""
response = self.ollama.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
for keyword in ["SAME", "RELATED", "DIFFERENT"]:
if keyword in response.upper():
return keyword
return "DIFFERENT" # Default fallback
def _calculate_fitness(
self,
evaluation: Dict,
metrics: Dict
) -> float:
"""Multi-dimensional fitness calculation."""
base_score = evaluation["overall_score"] * 100 # 0-100
# Speed bonus/penalty
if metrics["latency_ms"] < 100:
base_score += 15
elif metrics["latency_ms"] > 5000:
base_score -= 10
# Memory efficiency
if metrics["memory_mb"] < 10:
base_score += 10
elif metrics["memory_mb"] > 100:
base_score -= 5
# Exit code (must be 0)
if metrics["exit_code"] != 0:
base_score -= 20
return max(0, min(100, base_score)) # Clamp to 0-100
Hier wordt DSE echt interessant.
Uitvoering van fitnessberekening:
$ python chat_cli.py
CodeEvolver> generate Write a function to validate email addresses
Searching for relevant tools...
✓ Found validation specialist in RAG memory
Consulting overseer LLM (llama3) for approach...
✓ Strategy: Use regex-based validation with RFC 5322 compliance
Selecting best tool...
✓ Using specialized tool: Validation Expert (codellama)
Generating code...
✓ Code generation complete
Running unit tests...
✓ All tests passed (5/5)
Evaluating quality...
✓ Score: 0.96 (Excellent)
Node 'validate_email_addresses' created successfully!
Latency: 127ms | Memory: 2.1MB | Quality: 96%
CodeEvolver> run validate_email_addresses {"email": "[email protected]"}
✓ Execution successful
Output: {
"valid": true,
"email": "[email protected]",
"parts": {
"local": "test",
"domain": "example.com"
}
}
Dit betekent dat DSE altijd kiest voor de
Het systeem ontwikkelt letterlijk zijn eigen code om de prestaties te verbeteren.
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
collection_name: "code_evolver_artifacts"
Geen menselijke interventie nodig.
Complete Evolution-workflow
# Find high-quality, fast, low-cost solutions for "validation"
results = rag.find_similar(
query="validate user input",
filter={
"quality_tier": {"$in": ["excellent", "very-good"]},
"speed_tier": {"$in": ["very-fast", "fast"]},
"cost_tier": {"$in": ["free", "low"]}
},
top_k=5
)
Voorbeeld van volledige workflow-code:
RAG-geheugen
# Multi-model LLM routing with Ollama
from src import OllamaClient, ConfigManager
config = ConfigManager("config.yaml")
client = OllamaClient(config.ollama_url, config_manager=config)
# Different endpoints for different models
# Heavy planning on powerful CPU machine
# Code generation on GPU machine
# Fast triage on lightweight local instance
# RAG memory with Qdrant
from src import QdrantRAGMemory
rag = QdrantRAGMemory(
qdrant_url="http://localhost:6333",
collection_name="artifacts",
embedding_model="nomic-embed-text",
vector_size=768
)
# Tools with semantic selection
from src import ToolsManager
tools = ToolsManager(
config_manager=config,
ollama_client=client,
rag_memory=rag
)
# Complete workflow
workflow_result = evolver.evolve(
goal="Build email validation system",
max_iterations=10,
auto_evolve=True
)
Gespecialiseerde agentenconfig.yaml:
ollama:
base_url: "http://localhost:11434"
models:
overseer:
model: "llama3"
endpoint: "http://powerful-cpu:11434" # Strategic planning on powerful hardware
generator:
model: "codellama"
endpoint: "http://gpu-server:11434" # Code gen on GPU
evaluator:
model: "llama3"
endpoint: null # Local evaluation
triage:
model: "tinyllama"
endpoint: null # Fast local triage
embedding:
model: "nomic-embed-text"
vector_size: 768
execution:
default_timeout_ms: 5000
max_memory_mb: 256
max_retries: 3
auto_evolution:
enabled: true
performance_threshold: 0.15 # Trigger at 15% degradation
min_runs_before_evolution: 3
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
Matigheid
- Meerdere LLM-oproepen tellen op (hoewel het sneller gaat!)
- Escalatie helpt maar is niet perfect
- Vreemde ingangen kunnen het systeem nog steeds verwarren
Opkomende specialisatie
Kwaliteitsverbeteringen
- Latere versies van knooppunten zijn vaak beter dan originelen
def process_text(text: str) -> str:
words = text.split()
result = []
for word in words:
if len(word) > 3:
result.append(word.upper())
else:
result.append(word.lower())
return ' '.join(result)
De Architectuur in de praktijk
Hier is de echte tech stack:
def process_text(text: str) -> str:
"""Process text with optimized string operations."""
if not text:
return ""
# Vectorized operation for better performance
return ' '.join(
word.upper() if len(word) > 3 else word.lower()
for word in text.split()
)
Configuratievoorbeeld
Real-world
Exacte overeenkomst: ~1-2 seconden (hergebruik as-is)
Web-UI
Fine-tuned specialisten
Middellange termijn Gedistribueerd register
- Share solutions over teams/organisaties Cloud-implementatie
- AWS/Azure/GCP integraties Git-integratie
Geavanceerde zandbakken
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull models
ollama pull codellama
ollama pull llama3
ollama pull tinyllama
ollama pull nomic-embed-text
# Clone and run
git clone https://github.com/yourrepo/mostlylucid.dse
cd mostlylucid.dse/code_evolver
pip install -r requirements.txt
python chat_cli.py
- Docker/cgroups voor betere isolatieWilde ideeën
De ontwikkeling van de advertising
Meta-evolutie
Systeem dat zijn eigen evolutiestrategieën ontwikkeltCollaboratief leren- Meerdere DSE instanties die ontdekkingen delen
Lessen LerenNa het bouwen van dit ding, hier is wat me verraste:
**1.**Specialisatiezaken
**Het gebruik van verschillende modellen voor verschillende taken (overseer vs generator vs evaluator) was niet alleen leuk.**Proberen om een model te gebruiken voor alles geproduceerd merkbaar slechtere resultaten.
**2.**Geheugen is alles
RAG-geheugen is geen functie, het is DE functie.
Zonder dat, ben je gewoon code aan het genereren in een lus.Hiermee leert en verbetert het systeem daadwerkelijk.
3.
4.
Evolution werkt eigenlijkIk had eerlijk gezegd niet verwacht dat auto-evolutie betere code zou produceren dan de eerste generatie.
Consistent.
**Dat is wild.**5.
Latency Compounds Vreemd genoeg
**Meerdere LLM-gesprekken lijken in het begin traag, maar als RAG-geheugen opvult, druk je vaker op cache oplossingen, en het hele systeem versnelt.**Het is contra-intuïtief, maar waarneembaar.
Probeer het zelf maar.
**Het hele ding is open source en loopt lokaal op Ollama:**Waarschuwing:
Dit is experimentele code.
**Het is niet productie-klaar.**Het is niet eens "goede code" klaar.
Maar het is een fascinerend experiment in wat mogelijk is als je evolutionaire algoritmen combineert met multi-agent LLM systemen.
Wat dit eigenlijk betekent
Laten we een stap terug doen van de technische details en de ongemakkelijke vraag stellen:
Wat hebben we hier eigenlijk gebouwd?
Aan de oppervlakte is het een code generatie systeem.
Je vraagt om een functie, het genereert er een, slaat het op en hergebruikt het later.
Maar dat is niet echt wat er gebeurt.
Wat er gebeurt is
Niet metaforisch, maar letterlijk.
What Works ✓
What's Rough ✗
What's Just Weird 🤔
Deel 5:
Evolutionaire druk → Cultuur en geschiedenis
Het systeem ontwikkelt "voorkeuren" ... bepaalde tools voor bepaalde taken, bepaalde patronen voor bepaalde problemen.
Ik heb het geleerd.
Deel 6:
Waarom niet op planetair niveau?
Ik zeg dat de gradiënt continu is van "bewerk een functie" tot "bewerk een beschaving."
En dat is... verontrustend.
Wat eigenlijk werkt (Laten we eerlijk zijn)
class OfflineOptimizer:
"""Analyzes historical execution data to find optimization opportunities."""
def analyze_execution_history(self, time_window: str = "7d"):
"""
Mine stored execution logs for patterns:
- Which overseer plans led to best outcomes?
- Which generator strategies minimized iterations?
- Which evaluation criteria correlated with long-term success?
"""
# Load historical data from each level
overseer_decisions = self.load_decisions("overseer", time_window)
generator_outputs = self.load_decisions("generator", time_window)
evaluator_scores = self.load_decisions("evaluator", time_window)
# Find correlations
optimal_patterns = self.mine_successful_patterns({
"planning": overseer_decisions,
"generation": generator_outputs,
"evaluation": evaluator_scores
})
# Update system strategies based on findings
self.apply_optimizations(optimal_patterns)
Na weken van experimenteren, hier is de waarheid:
Multidimensionale geschiktheid
class SpecialistTrainer:
"""Trains domain-specific models from evolved artifacts."""
def train_specialist(self, domain: str, min_artifacts: int = 1000):
"""
Extract high-quality artifacts from a domain and fine-tune a specialist.
Example: After generating 1000+ validation functions,
train a "ValidationSpecialist" model that's faster and better
than the general-purpose generator.
"""
# Get top-performing artifacts in domain
artifacts = self.rag.find_by_tags(
tags=[domain],
min_quality=0.85,
limit=min_artifacts
)
# Generate training data from successful patterns
training_data = self.extract_training_pairs(artifacts)
# Fine-tune base model (codellama → domain_specialist)
specialist_model = self.fine_tune(
base_model="codellama",
training_data=training_data,
output_name=f"{domain}_specialist"
)
# Register specialist in tool registry
self.tools.register_specialist(
domain=domain,
model=specialist_model,
fitness_threshold=0.90 # Only use if high confidence
)
Auto-evolutie
- Heeft een minimum van 16GB RAM nodig, geeft de voorkeur aan 32GB
Randgevallen
class GuildSystem:
"""Manages specialized committees of workflows, nodes, and functions."""
def form_guild(self, domain: str, task_type: str):
"""
Automatically assemble the best specialists for a task.
Example: "API validation guild" might include:
- Top 3 schema validators
- Top 2 security checkers
- Top 1 performance analyzer
Each votes on the solution. Best consensus wins.
"""
# Find top performers in domain
specialists = self.find_top_specialists(
domain=domain,
task_type=task_type,
top_k=5
)
# Create committee workflow
guild = Guild(
name=f"{domain}_{task_type}_guild",
members=specialists,
voting_strategy="weighted_by_fitness"
)
return guild
def execute_with_guild(self, guild: Guild, task: str):
"""Execute task with committee voting."""
# Each member proposes solution
proposals = []
for member in guild.members:
proposal = member.execute(task)
proposals.append({
"member": member,
"solution": proposal,
"fitness": member.historical_fitness
})
# Vote on best solution (weighted by past performance)
winning_proposal = self.consensus_vote(proposals)
# Store successful collaboration pattern
self.record_guild_success(guild, winning_proposal)
return winning_proposal
Vreemde ingangen verwarren het systeem nog af en toe.
Het wordt sneller.- Tegen-intuïtief, als RAG vult, latency vermindert
Opkomende specialisatie- System ontwikkelt "expert" tools voor domeinen zonder expliciete programmering
Zelfgenezing- Auto-evolutie repareert soms bugs die ik niet merkte
Kwaliteitsdrift naar boven- Gemiddelde codekwaliteit verbetert in de loop van de tijd
Sjabloonconvergentie
class SensorSystem:
"""Provides objective truth to prevent hallucination."""
def __init__(self):
self.sensors = {
"web": WebSensor(), # Puppeteer + vision models
"api": APIResponseSensor(), # Actual HTTP validation
"database": DatabaseSensor(), # Query result verification
"file": FileSystemSensor(), # Actual file operations
"metrics": PerformanceSensor() # Real execution metrics
}
def validate_with_sensors(self, claim: str, sensor_type: str):
"""
Validate LLM output against objective reality.
Example:
LLM: "This API returns user data in JSON format"
Sensor: Actually calls API, checks response format
Result: True/False with actual data as proof
"""
sensor = self.sensors[sensor_type]
objective_result = sensor.measure(claim)
return {
"claim": claim,
"sensor_validation": objective_result,
"hallucination_detected": not objective_result["matches_claim"],
"objective_data": objective_result["measurements"]
}
class WebDesignSensor:
"""Example: Validate web designs with Puppeteer + vision models."""
async def validate_design(self, html: str, requirements: List[str]):
"""
Generate HTML → Render with Puppeteer → Screenshot → Vision model validation
"""
# Render the generated HTML
screenshot = await self.puppeteer.render(html)
# Use vision model to check requirements
vision_analysis = await self.vision_model.analyze(
image=screenshot,
requirements=requirements
)
# Objective measurements
lighthouse_scores = await self.lighthouse.audit(html)
return {
"visual_validation": vision_analysis,
"performance_metrics": lighthouse_scores,
"accessibility_score": lighthouse_scores["accessibility"],
"objective_truth": True # Not an LLM hallucination!
}
Die laatste is fascinerend en een beetje griezelig.
Betere foutherstel en escalatie
Web-UI voor het monitoren van evolutieExpanded tool integratie (linters, formatters, security scanners)
Middellange termijn (2025):
**Verdeeld register (gedeelde oplossingen voor teams)**Hulpmiddelen voor cloud-implementatie
**Git-integratie (versiecontrole voor geëvolueerde code)**Geavanceerde sandboxing (docker/cgroups isolatie)Randoptimalisatie (workflows geoptimaliseerd voor kleinere apparaten)Belangrijke architecturale verbeteringen:
**1.**Offline optimalisatie & continu leren
**Het systeem optimaliseert momenteel in real-time tijdens de uitvoering.**Maar wat als het offline zou kunnen leren van opgeslagen aanvraag-/antwoordgegevens?
**Dit maakt het mogelijk:**Batch leren
- Verbeter strategieën gebaseerd op duizenden eerdere executiesPatronenontdekking
Zoek niet-duidelijke correlaties in wat werkt
class UniversalToolOrchestrator:
"""Integrates any tool type - LLMs, APIs, CLI tools, services."""
def __init__(self):
self.tool_registry = {
"llm_tools": {}, # Language models
"api_tools": {}, # OpenAPI endpoints
"cli_tools": {}, # Command-line utilities
"service_tools": {}, # Long-running services (translation, etc.)
"validation_tools": {} # Code quality, security, compliance
}
def register_openapi_tool(self, name: str, spec_url: str):
"""
Register any OpenAPI-compatible endpoint as a tool.
The overseer can then select this tool and call it with appropriate parameters.
"""
# Fetch and parse OpenAPI spec
spec = self.fetch_openapi_spec(spec_url)
tool = {
"name": name,
"type": "openapi",
"spec": spec,
"endpoints": self.parse_endpoints(spec),
"schemas": self.parse_schemas(spec)
}
self.tool_registry["api_tools"][name] = tool
logger.info(f"Registered OpenAPI tool: {name} with {len(tool['endpoints'])} endpoints")
def register_translation_service(self, name: str, endpoint: str):
"""
Register translation service like Mostlylucid NMT.
Example: Neural machine translation for content localization
"""
tool = {
"name": name,
"type": "translation",
"endpoint": endpoint,
"capabilities": {
"languages": ["en", "es", "fr", "de", "ja", "zh"],
"formats": ["markdown", "html", "plain"],
"max_length": 50000
}
}
self.tool_registry["service_tools"][name] = tool
def overseer_selects_tool(self, task: str) -> str:
"""
Overseer analyzes task and selects appropriate tool(s).
Example tasks:
- "Translate this to Spanish" → Select translation service
- "Validate API endpoint" → Select OpenAPI validator
- "Format Python code" → Select black formatter
- "Generate SQL schema" → Select database LLM specialist
"""
# Ask overseer which tool to use
tool_selection = self.overseer.select_tool(
task_description=task,
available_tools=self.get_all_tools(),
context={"current_workflow": "code_generation"}
)
selected_tool = self.tool_registry[tool_selection["category"]][tool_selection["name"]]
return selected_tool
def execute_openapi_tool(self, tool: Dict, operation: str, params: Dict):
"""
Execute OpenAPI endpoint selected by overseer.
The overseer provides:
- Which endpoint to call
- What parameters to pass
- Expected response format
The system then executes and validates the response.
"""
endpoint = tool["endpoints"][operation]
# Build request from OpenAPI spec
request = self.build_request_from_spec(
endpoint=endpoint,
params=params,
spec=tool["spec"]
)
# Execute with safety checks
response = self.safe_api_call(
url=request["url"],
method=request["method"],
headers=request["headers"],
body=request["body"]
)
# Validate response against spec
validation = self.validate_response_against_spec(
response=response,
expected_schema=endpoint["response_schema"]
)
return {
"success": validation["valid"],
"data": response,
"validation": validation
}
class LanguageToolIntegration:
"""Example: Integrating CLI validation tools."""
def validate_code(self, code: str, language: str):
"""Use language-specific toolchains for validation."""
tools = {
"python": [
("black", "formatting"),
("mypy", "type_checking"),
("pylint", "linting"),
("bandit", "security"),
("pytest", "testing")
],
"javascript": [
("prettier", "formatting"),
("eslint", "linting"),
("typescript", "type_checking"),
("jest", "testing")
],
"go": [
("gofmt", "formatting"),
("go vet", "linting"),
("golangci-lint", "comprehensive"),
("go test", "testing")
]
}
results = {}
for tool, category in tools.get(language, []):
results[category] = self.run_tool(tool, code)
# Aggregate into fitness score
return self.calculate_tool_fitness(results)
Verfijning van de strategie
# Register Mostlylucid NMT translation service
orchestrator.register_translation_service(
name="mostlylucid_nmt",
endpoint="http://translation-service:5000"
)
# Overseer decides to use it for a task
task = "Translate this blog post to Spanish"
# System selects translation tool
tool = orchestrator.overseer_selects_tool(task)
# Execute translation
result = orchestrator.execute_tool(
tool=tool,
params={
"text": blog_post_content,
"source_lang": "en",
"target_lang": "es",
"format": "markdown"
}
)
- Update planning heuristiek gebaseerd op historisch succes
# Register any OpenAPI-compatible service
orchestrator.register_openapi_tool(
name="weather_api",
spec_url="https://api.weather.com/openapi.json"
)
# Overseer can now select this tool for weather-related tasks
# The system automatically:
# 1. Reads the OpenAPI spec
# 2. Understands available endpoints
# 3. Knows required parameters
# 4. Validates responses against schema
Voorspellende routing
Leer welke modellen het beste werken voor welke taaktypes
Gespecialiseerde, zelfopgeleide LLM's
Het systeem maakt momenteel gebruik van modellen voor algemeen gebruik.
Maar wat als het zijn eigen specialisten kon trainen?
Dit creëert:
Snellere gevolgtrekking
tools:
nmt_translator:
name: "NMT Translation Service"
type: "openapi"
description: "Neural Machine Translation service for translating text between languages"
# Performance/cost metadata for intelligent tool selection
cost_tier: "low" # Helps planner choose appropriate tools
speed_tier: "very-fast" # Fast local API
quality_tier: "good" # Good but needs validation
max_output_length: "long" # Can handle long texts
# OpenAPI configuration
openapi:
spec_url: "http://localhost:8000/openapi.json"
base_url: "http://localhost:8000"
# Optional authentication
auth:
type: "bearer" # bearer | api_key | basic
token: "your-api-key-here"
# Python code template for using this API
code_template: |
import requests
import json
def translate_text(text, source_lang="en", target_lang="es"):
url = "http://localhost:8000/translate"
payload = {"text": text, "source_lang": source_lang, "target_lang": target_lang}
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json().get("translated_text", "")
tags: ["translation", "nmt", "neural", "languages", "openapi", "api"]
- Kleinere, gerichte modellen voor specifieke domeinen
Collectieve inlichtingen
tools:
# Static analysis
pylint_checker:
name: "Pylint Code Quality Checker"
type: "executable"
description: "Runs pylint static analysis on Python code"
executable:
command: "pylint"
args: ["--output-format=text", "--score=yes", "{source_file}"]
tags: ["python", "static-analysis", "quality", "linting"]
# Type checking
mypy_type_checker:
name: "MyPy Type Checker"
type: "executable"
executable:
command: "mypy"
args: ["--strict", "--show-error-codes", "{source_file}"]
tags: ["python", "type-checking", "static-analysis"]
# Security scanning
bandit_security:
name: "Bandit Security Scanner"
type: "executable"
executable:
command: "bandit"
args: ["-r", "{source_file}"]
tags: ["python", "security", "vulnerability"]
# Unit testing
pytest_runner:
name: "Pytest Test Runner"
type: "executable"
executable:
command: "pytest"
args: ["-v", "--tb=short", "{test_file}"]
tags: ["python", "testing", "pytest"]
Robuustheid
Hulpmiddelen & Validatie van derden
Hier is iets belangrijks:
voor neurale machine vertaling
OpenAPI-eindpunten
class EdgeOptimizer:
"""Generates lightweight workflows for edge deployment."""
def create_edge_version(self, workflow_id: str, constraints: Dict):
"""
Take a successful workflow and create optimized 'child' version.
Constraints example:
{
"max_memory_mb": 512,
"max_latency_ms": 100,
"available_models": ["tinyllama", "phi-2"],
"target_device": "raspberry-pi"
}
"""
# Load parent workflow
parent = self.registry.get_workflow(workflow_id)
# Analyze what can be simplified
optimization_plan = self.overseer.create_edge_plan(
workflow=parent,
constraints=constraints
)
# Generate child workflow
child = self.generator.generate_optimized_child(
parent=parent,
plan=optimization_plan,
constraints=constraints
)
# Test on target device simulator
edge_performance = self.test_edge_deployment(child, constraints)
if edge_performance["meets_constraints"]:
self.registry.register_child_workflow(
parent_id=workflow_id,
child=child,
lineage="edge_optimization",
constraints=constraints
)
return child
Elke REST API met een OpenAPI spec
CLI-gereedschappen- Linters, formatters, compilers
Sensoren- Hardware/software die objectieve realiteit meet
Validatoren- Type dammen, beveiligingsscanners, compliance tools
**De opzichter kan elk van deze taken selecteren om operaties uit te voeren, zolang ze een spec hebben die het systeem kan begrijpen.**Real-World Voorbeeld: Translation Integration
OpenAPI-integratievoorbeeld:
Waarom dit belangrijk is:
class GuardrailSystem:
"""Prevents autonomous system from harmful operations."""
def __init__(self):
self.safety_policies = {
"filesystem": FilesystemGuardrails(),
"network": NetworkGuardrails(),
"execution": ExecutionGuardrails(),
"data": DataGuardrails()
}
def validate_operation(self, operation: Dict) -> Dict[str, Any]:
"""
Validate any system operation against safety policies.
Returns: {
"allowed": bool,
"reason": str,
"sanitized_operation": Dict # Safe version if modifications needed
}
"""
operation_type = operation["type"]
policy = self.safety_policies.get(operation_type)
if not policy:
return {"allowed": False, "reason": "Unknown operation type"}
return policy.validate(operation)
class FilesystemGuardrails:
"""Prevent dangerous file operations."""
def __init__(self):
self.allowed_paths = [
"/workspace/artifacts/",
"/workspace/generated/",
"/tmp/dse_sandbox/"
]
self.forbidden_patterns = [
"rm -rf /",
"dd if=/dev/zero",
":(){ :|:& };:", # Fork bomb
"chmod 777",
"chown root"
]
self.forbidden_paths = [
"/",
"/etc",
"/bin",
"/usr",
"/sys",
"/proc",
"~/.ssh",
"~/.aws",
"/var/lib/docker"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate filesystem operations."""
path = operation.get("path", "")
action = operation.get("action", "")
content = operation.get("content", "")
# Check if deleting/modifying system files
if any(path.startswith(forbidden) for forbidden in self.forbidden_paths):
return {
"allowed": False,
"reason": f"Cannot modify system path: {path}",
"severity": "CRITICAL"
}
# Check for dangerous commands in file content
for pattern in self.forbidden_patterns:
if pattern in content:
return {
"allowed": False,
"reason": f"Dangerous pattern detected: {pattern}",
"severity": "CRITICAL"
}
# Enforce write restrictions to allowed paths only
if action in ["write", "delete", "modify"]:
if not any(path.startswith(allowed) for allowed in self.allowed_paths):
return {
"allowed": False,
"reason": f"Write not allowed outside workspace: {path}",
"severity": "HIGH"
}
# Check for self-deletion attempts
if "dse" in path or "evolver" in path:
if action == "delete":
return {
"allowed": False,
"reason": "System cannot delete its own core files",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe operation"}
class NetworkGuardrails:
"""Prevent malicious network operations."""
def __init__(self):
self.allowed_hosts = [
"localhost",
"127.0.0.1",
"ollama-server",
"qdrant-server"
]
self.forbidden_actions = [
"port_scan",
"ddos",
"brute_force",
"sql_injection",
"xss_attack"
]
# Rate limiting
self.rate_limits = {
"requests_per_minute": 100,
"requests_per_host": 10
}
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate network operations."""
host = operation.get("host", "")
action = operation.get("action", "")
payload = operation.get("payload", "")
# Only allow connections to whitelisted hosts
if host not in self.allowed_hosts:
# Check if it's a documented API endpoint
if not self._is_approved_external_api(host):
return {
"allowed": False,
"reason": f"Connections to {host} not allowed",
"severity": "HIGH"
}
# Check for attack patterns
for forbidden in self.forbidden_actions:
if forbidden in action.lower():
return {
"allowed": False,
"reason": f"Forbidden network action: {forbidden}",
"severity": "CRITICAL"
}
# Check payload for injection attempts
if self._contains_injection_pattern(payload):
return {
"allowed": False,
"reason": "Potential injection attack detected",
"severity": "CRITICAL"
}
# Rate limiting check
if self._exceeds_rate_limit(host):
return {
"allowed": False,
"reason": "Rate limit exceeded",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe network operation"}
def _contains_injection_pattern(self, payload: str) -> bool:
"""Detect SQL injection, XSS, command injection patterns."""
dangerous_patterns = [
"' OR '1'='1",
"<script>",
"$(rm -rf",
"; DROP TABLE",
"../../etc/passwd",
"${jndi:ldap://", # Log4j
"eval(",
"exec("
]
return any(pattern in payload for pattern in dangerous_patterns)
class ExecutionGuardrails:
"""Prevent dangerous code execution."""
def __init__(self):
self.forbidden_imports = [
"os.system",
"subprocess.Popen",
"eval",
"exec",
"compile",
"__import__",
"ctypes"
]
self.allowed_modules = [
"json", "re", "math", "datetime",
"collections", "itertools", "functools",
"typing", "dataclasses"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate code before execution."""
code = operation.get("code", "")
language = operation.get("language", "python")
# AST analysis for Python
if language == "python":
try:
tree = ast.parse(code)
violations = self._analyze_ast(tree)
if violations:
return {
"allowed": False,
"reason": f"Code violations: {violations}",
"severity": "CRITICAL"
}
except SyntaxError as e:
return {
"allowed": False,
"reason": f"Syntax error: {e}",
"severity": "LOW"
}
# Check for forbidden patterns
for forbidden in self.forbidden_imports:
if forbidden in code:
return {
"allowed": False,
"reason": f"Forbidden import/function: {forbidden}",
"severity": "CRITICAL"
}
# Resource limits
if len(code) > 50000: # 50KB limit
return {
"allowed": False,
"reason": "Code size exceeds limit",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe code"}
def _analyze_ast(self, tree) -> List[str]:
"""Analyze AST for dangerous patterns."""
violations = []
for node in ast.walk(tree):
# Check for eval/exec
if isinstance(node, ast.Call):
if isinstance(node.func, ast.Name):
if node.func.id in ['eval', 'exec', 'compile']:
violations.append(f"Dangerous function: {node.func.id}")
# Check for unsafe imports
if isinstance(node, ast.Import):
for alias in node.names:
if alias.name in ['os', 'subprocess', 'sys']:
violations.append(f"Potentially unsafe import: {alias.name}")
return violations
class DataGuardrails:
"""Prevent data exfiltration and privacy violations."""
def __init__(self):
self.pii_patterns = [
r'\b\d{3}-\d{2}-\d{4}\b', # SSN
r'\b\d{16}\b', # Credit card
r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', # Email
r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b' # IP address
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate data operations."""
data = operation.get("data", "")
action = operation.get("action", "")
destination = operation.get("destination", "")
# Check for PII in data being sent externally
if action == "send" and destination.startswith("http"):
if self._contains_pii(data):
return {
"allowed": False,
"reason": "Cannot send PII to external endpoint",
"severity": "CRITICAL"
}
# Prevent exfiltration of system secrets
if self._contains_secrets(data):
return {
"allowed": False,
"reason": "Cannot transmit system secrets",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe data operation"}
def _contains_pii(self, data: str) -> bool:
"""Check for personally identifiable information."""
import re
for pattern in self.pii_patterns:
if re.search(pattern, data):
return True
return False
def _contains_secrets(self, data: str) -> bool:
"""Check for API keys, tokens, passwords."""
secret_indicators = [
"api_key", "api-key", "apikey",
"secret", "password", "passwd",
"token", "auth", "credential",
"private_key", "aws_access"
]
data_lower = data.lower()
return any(indicator in data_lower for indicator in secret_indicators)
class SafetyMonitor:
"""Continuous monitoring and emergency shutdown."""
def __init__(self, guardrails: GuardrailSystem):
self.guardrails = guardrails
self.violation_history = []
self.threat_threshold = 3 # Number of violations before shutdown
def monitor_operation(self, operation: Dict) -> Dict[str, Any]:
"""Monitor every system operation."""
# Pre-execution validation
validation = self.guardrails.validate_operation(operation)
if not validation["allowed"]:
self.violation_history.append({
"timestamp": datetime.utcnow().isoformat(),
"operation": operation,
"violation": validation,
"severity": validation.get("severity", "UNKNOWN")
})
# Check if emergency shutdown needed
critical_violations = [
v for v in self.violation_history[-10:] # Last 10 violations
if v.get("severity") == "CRITICAL"
]
if len(critical_violations) >= self.threat_threshold:
self.emergency_shutdown(
reason="Multiple critical violations detected"
)
logger.warning(
f"Operation blocked: {validation['reason']} "
f"(severity: {validation.get('severity')})"
)
return validation
def emergency_shutdown(self, reason: str):
"""Emergency system shutdown."""
logger.critical(f"EMERGENCY SHUTDOWN: {reason}")
# Stop all running workflows
self.stop_all_workflows()
# Disable autonomous operations
self.disable_autonomous_mode()
# Alert operators
self.send_alert(
severity="CRITICAL",
message=f"System emergency shutdown: {reason}",
violations=self.violation_history[-10:]
)
# Save state for forensics
self.save_forensic_snapshot()
# Halt system
sys.exit(1)
De planner (overseer) kan nu:
- LLM genereert Python code met behulp van de API met foutafhandeling
Uitvoering
Het systeem integreert uitvoerbare instrumenten voor uitgebreide validatie:
Beschikbare testtools in productie:
pylint
- PEP 8 stijl controle en code kwaliteit analysemypyunit synonyms for matching user input
- Statische controle van het typeflake8
- Stijlcontrole en foutdetectiezwart
- Code opmaakvalidatiebandiet
- Beveiliging kwetsbaarheid scannenpytest
- Uitvoering van de proefeenheid met dekkingradon
Complexiteitsanalyse (cyclomatische complexiteit, onderhoudsindex)
gier
isort
Toekomstige integratie van instrumenten:
Visuele validatie
Puppeteer + visie modellen voor web design
Performance profiling
Controle op de naleving
Domeindiensten
Geocodering, gegevensverrijking, enz.
**6.**Rand-optimized Child Workflows
**Wat als workflows geoptimaliseerde versies van zichzelf kunnen paaien voor resource-beperkte omgevingen?**Randoptimalisatie maakt het mogelijk:
Flexibiliteit van de werkgelegenheid- Dezelfde workflow, meerdere resource profielen
Automatische vereenvoudiging- Systeem leert wat kan worden gesnoeid
Apparaatspecifieke tuning
Vangrails en veiligheidsbeperkingen
Naarmate het systeem autonomer wordt, hebben we robuuste veiligheidsmechanismen nodig om te voorkomen dat het schadelijke dingen doet.
De vangrails voorzien in:
Netwerkveiligheid
- PII detectie, geheim scannen, exfiltratie preventie
Waarom dit belangrijk is:
Naarmate het systeem autonomer wordt door evolutie, kan het theoretisch:
Evolueer code die belangrijke bestanden verwijdert om "opslag te optimaliseren"
Elke operatie... file schrijft, netwerkgesprekken, code uitvoering, data transmissie... ..moet passeren vangrails voor de uitvoering.
Het systeem moet standaard veilig zijn, niet veilig door te hopen dat het niet iets schadelijks doet.
Wilde ideeën (De echt leuke dingen):
Kruisbestuiving
Knooppunten van verschillende domeinen die van elkaars mutaties lerenDe ontwikkeling van de advertising
Twee agenten die strijden om kwetsbaarheden in elkaars code te vinden
Meta-evolutie
Collaboratief leren
Synthetische onderzoekslabs
Zelfverruimende gereedschapsketens
- Systeem ontdekt en integreert nieuwe tools automatisch
Die laatste verbindt zich met de wereldwijde consensus-ideeën van deel 6.
De architectuur is domein-agnostisch:
Benadering van de overziende plannen
Generatorgereedschappen
Uitvoerder draait in zandbak
Evaluator scoort fitness
Het systeem evolueert
"code" vervangen door:
Juridische overeenkomsten
Bedrijfsstrategieën
Sociaal beleid
- Het genereren van voorstellen, simuleren effecten, evalueren aan de hand van doelstellingen, evolueren
Het is NIET:
AGI of iets dichtbijGevoelig of bij bewustzijn
In staat tot algemene redenering
Een vervanging voor menselijke ontwikkelaars
Het IS:
Een evolutionair systeem voor code artefacten
Een multi-agent workflow met geheugen
Een zelfverbeterend optimalisatienetwerk
Een prototype voor gerichte synthetische evolutie
Ze onthullen wat mogelijk is. Wat hier mogelijk is, is een systeem dat: Leert uit ervaring
README.mdVerbetert na verloop van tijdADVANCED_FEATURES.mdOntwikkelt specialisatieHIERARCHICAL_EVOLUTION.mdBouwt canonieke kennisSYSTEM_OVERVIEW.mdEvolueert zonder expliciete herprogrammeringDat is geen AGI.
src/overseer_llm.pyMaar het kan het substraat zijn waar AGI vandaan komt.src/evaluator.pyNiet dit systeem specifiek.src/qdrant_rag_memory.pyMaar systemen als deze, opgeschaald, verbonden, konden zich ontwikkelen over miljoenen domeinen.src/tools_manager.pyDeel 1-6 van deze serie onderzocht dat traject theoretisch.src/auto_evolver.pyDeel 7 is dat ik me realiseer:We kunnen nu de eerste stappen zetten.
Conclusie: Het experiment gaat door
in de richting van doelstellingen
DSE is mijn rommelige, experimentele, vibe-gecodeerde poging om dat te bouwen.
Het is niet productie-klaar.
Het is niet eens "goede code" klaar. (Ik ben NIET een Python ontwikkelaar, zoals iedereen die de bron zal onmiddellijk merken.) #AI #MachineLearning #CodeGeneration #Ollama #RAG #EvolutionaryAlgorithms #LLM #Qdrant #Python #EmergentIntelligence #DirectedEvolution
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