turbo

name	turbo
description	Direct code generation via hosted LLM (Cerebras). Write a contract prompt, generate code, fix surgically. Part of speed-run pipeline.

Turbo

Direct code generation via hosted LLM. Claude writes the contract, Cerebras implements the code, files are written directly to disk.

Announce: "I'm using speed-run:turbo for hosted code generation."

When to Use

Use turbo for:

• Algorithmic code (rate limiters, parsers, state machines)
• Multiple files (3+)
• Boilerplate-heavy implementations
• Token-constrained sessions

Use Claude direct instead for:

• CRUD/storage operations (Claude is cheaper due to no fix overhead)
• Single implementation with complex coordination
• Speed-critical tasks where fix cycles are costly

Tradeoffs

Aspect	Claude Direct	Turbo (Hosted LLM)
Speed	~10s	~0.5s
Token Cost	Higher	~90% savings
First-pass Quality	~100%	80-95%
Fixes Needed	0	0-2 typical

Workflow

Step 1: Write Contract Prompt

Structure your prompt with exact specifications:

Build [X] with [tech stack].

## DATA CONTRACT (use exactly these models):

[Pydantic models / interfaces with exact field names and types]

Example:
class Task(BaseModel):
    id: str
    title: str
    completed: bool = False
    created_at: datetime

class TaskCreate(BaseModel):
    title: str

## API CONTRACT (use exactly these routes):

POST /tasks -> Task           # Create task
GET /tasks -> list[Task]      # List all tasks
GET /tasks/{id} -> Task       # Get single task
DELETE /tasks/{id} -> dict    # Delete task
POST /reset -> dict           # Reset state (for testing)

## ALGORITHM:

1. [Step-by-step logic for the implementation]
2. [Include state management details]
3. [Include edge case handling]

## RULES:

- Use FastAPI with uvicorn
- Store data in [storage mechanism]
- Return 404 for missing resources
- POST /reset must clear all state and return {"status": "ok"}

Step 2: Generate Code

mcp__speed-run__generate_and_write_files
  prompt: [contract prompt]
  output_dir: [target directory]

Returns only metadata (files written, line counts). Claude never sees the generated code.

Step 3: Run Tests

Run the test suite against generated code.

Step 4: Fix (if needed)

For failures, use Claude Edit tool for surgical fixes (typically 1-4 lines each).

Common fixes:

Error Type	Frequency	Fix Complexity
Missing utility functions	Occasional	4 lines
Logic edge cases	Occasional	1-2 lines
Import ordering	Rare	1 line

Step 5: Re-test

Repeat Steps 3-4 until all tests pass. Even with fixes, total token cost is much lower than Claude generating everything.

What Hosted LLM Gets Right (~90%)

• Data models match contract exactly
• Routes/endpoints correct
• Core algorithm logic
• Basic error handling

Configuration

Variable	Default	Description
CEREBRAS_API_KEY	(required)	Your API key
CEREBRAS_MODEL	gpt-oss-120b	Model to use

Available models:

Model	Price (in/out)	Speed	Notes
gpt-oss-120b	$0.35/$0.75	3000 t/s	Default - best value, clean output
llama-3.3-70b	$0.85/$1.20	2100 t/s	Reliable fallback
qwen-3-32b	$0.40/$0.80	2600 t/s	Has verbose <think> tags
llama3.1-8b	$0.10/$0.10	2200 t/s	Cheapest, may need more fixes