Opinion: Why Local AI Dev Assistants Like Ollama 0.5 Are Overrated for Most Developers

import os
import time
import json
import ollama
import requests
from typing import List, Dict, Optional
from dataclasses import dataclass

# Configuration constants
OLLAMA_MODEL = "llama3.1:8b"  # Ollama 0.5 default supported model
OPENROUTER_API_URL = "https://openrouter.ai/api/v1/chat/completions"
HUMAN_EVAL_SAMPLE_PATH = "humaneval_sample_100.json"  # 100 curated HumanEval tasks
BENCHMARK_RESULTS_PATH = "benchmark_results.json"

@dataclass
class BenchmarkTask:
    task_id: str
    prompt: str
    canonical_solution: str
    test_cases: List[str]

def load_benchmark_tasks(path: str) -> List[BenchmarkTask]:
    """Load curated HumanEval sample tasks from JSON file."""
    try:
        with open(path, 'r') as f:
            raw_tasks = json.load(f)
        return [
            BenchmarkTask(
                task_id=t["task_id"],
                prompt=t["prompt"],
                canonical_solution=t["canonical_solution"],
                test_cases=t["test_cases"]
            ) for t in raw_tasks
        ]
    except FileNotFoundError:
        raise RuntimeError(f"Benchmark task file not found at {path}")
    except json.JSONDecodeError:
        raise RuntimeError(f"Invalid JSON in benchmark task file {path}")

def query_ollama(prompt: str, model: str = OLLAMA_MODEL, timeout: int = 30) -> Optional[str]:
    """Query local Ollama 0.5 instance with error handling."""
    try:
        response = ollama.chat(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            options={"temperature": 0.2, "num_predict": 512}
        )
        return response["message"]["content"]
    except ollama.ResponseError as e:
        print(f"Ollama error: {e.status_code} - {e.error}")
        return None
    except Exception as e:
        print(f"Unexpected Ollama error: {str(e)}")
        return None

def query_cloud_api(prompt: str, timeout: int = 30) -> Optional[str]:
    """Query cloud LLM API (OpenRouter) with error handling."""
    api_key = os.getenv("OPENROUTER_API_KEY")
    if not api_key:
        raise RuntimeError("OPENROUTER_API_KEY environment variable not set")

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "openai/gpt-4o-mini",  # Comparable cost/size to local 8B models
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.2,
        "max_tokens": 512
    }

    try:
        response = requests.post(OPENROUTER_API_URL, headers=headers, json=payload, timeout=timeout)
        response.raise_for_status()
        return response.json()["choices"][0]["message"]["content"]
    except requests.exceptions.Timeout:
        print("Cloud API request timed out")
        return None
    except requests.exceptions.HTTPError as e:
        print(f"Cloud API HTTP error: {e.response.status_code} - {e.response.text}")
        return None
    except Exception as e:
        print(f"Unexpected cloud API error: {str(e)}")
        return None

def run_benchmark(tasks: List[BenchmarkTask]) -> Dict:
    """Run full benchmark comparing Ollama and cloud API."""
    results = {
        "ollama": {"success": 0, "total": 0, "latencies": []},
        "cloud": {"success": 0, "total": 0, "latencies": []}
    }

    for task in tasks:
        # Benchmark Ollama
        start = time.time()
        ollama_resp = query_ollama(task.prompt)
        ollama_lat = time.time() - start
        results["ollama"]["total"] += 1
        results["ollama"]["latencies"].append(ollama_lat)
        if ollama_resp and validate_response(ollama_resp, task):
            results["ollama"]["success"] += 1

        # Benchmark cloud API
        start = time.time()
        cloud_resp = query_cloud_api(task.prompt)
        cloud_lat = time.time() - start
        results["cloud"]["total"] += 1
        results["cloud"]["latencies"].append(cloud_lat)
        if cloud_resp and validate_response(cloud_resp, task):
            results["cloud"]["success"] += 1

        time.sleep(1)  # Rate limit to avoid Ollama overload

    return results

def validate_response(response: str, task: BenchmarkTask) -> bool:
    """Check if generated code passes task test cases."""
    # Simplified validation: check if canonical solution keywords exist
    # In production, use exec with test cases in sandbox
    return task.canonical_solution.split("\n")[0] in response

if __name__ == "__main__":
    print("Loading benchmark tasks...")
    tasks = load_benchmark_tasks(HUMAN_EVAL_SAMPLE_PATH)
    print(f"Loaded {len(tasks)} tasks. Starting benchmark...")

    results = run_benchmark(tasks)

    with open(BENCHMARK_RESULTS_PATH, 'w') as f:
        json.dump(results, f, indent=2)

    print(f"Benchmark complete. Results saved to {BENCHMARK_RESULTS_PATH}")
    print(f"Ollama pass rate: {results['ollama']['success']/results['ollama']['total']:.1%}")
    print(f"Cloud API pass rate: {results['cloud']['success']/results['cloud']['total']:.1%}")

import argparse
import sys
from typing import Dict, List
from dataclasses import dataclass
import json

@dataclass
class HardwareSpec:
    name: str
    vram_gb: int
    purchase_price_usd: float
    power_watts: int
    lifespan_years: int

@dataclass
class CloudPlan:
    name: str
    monthly_price_usd: int
    requests_per_month: int
    model_context_window: int

# Predefined hardware options for running Ollama
LOCAL_HARDWARE_OPTIONS = [
    HardwareSpec("RTX 4060 Ti 16GB", 16, 449, 165, 4),
    HardwareSpec("RTX 4090 24GB", 24, 1599, 450, 4),
    HardwareSpec("MacBook Pro M3 Max 128GB", 128, 3499, 60, 5)
]

# Cloud AI assistant plans (2024 pricing)
CLOUD_PLANS = [
    CloudPlan("GitHub Copilot Business", 19, 10000, 16384),
    CloudPlan("OpenRouter GPT-4o-mini", 0.15, 1000, 128000),  # $0.15 per 1M input tokens, ~$0.15 per 1k requests
    CloudPlan("AWS CodeWhisperer Pro", 19, 10000, 8192)
]

def calculate_local_tco(hardware: HardwareSpec, monthly_power_cost_usd: float = 0.15) -> Dict:
    """Calculate 3-year TCO for local Ollama deployment."""
    try:
        # Depreciation: straight-line over lifespan
        annual_depreciation = hardware.purchase_price_usd / hardware.lifespan_years
        # Power cost: watts / 1000 * 24 hours * 30 days * power rate * 12 months
        annual_power_cost = (hardware.power_watts / 1000) * 24 * 30 * monthly_power_cost_usd * 12
        # Maintenance: 10% of purchase price per year
        annual_maintenance = hardware.purchase_price_usd * 0.1

        tco_3yr = (annual_depreciation + annual_power_cost + annual_maintenance) * 3
        return {
            "hardware": hardware.name,
            "3yr_tco_usd": round(tco_3yr, 2),
            "annual_cost_usd": round(tco_3yr / 3, 2),
            "monthly_cost_usd": round(tco_3yr / 36, 2)
        }
    except Exception as e:
        print(f"TCO calculation error: {str(e)}", file=sys.stderr)
        return {}

def calculate_cloud_tco(plan: CloudPlan, requests_per_month: int, months: int = 36) -> Dict:
    """Calculate cloud TCO for given number of requests."""
    try:
        if "per_month" in plan.name.lower():
            # Flat monthly rate
            total_cost = plan.monthly_price_usd * months
        else:
            # Token-based pricing: assume 500 tokens per request
            tokens_per_request = 500
            total_tokens = requests_per_month * tokens_per_request * months
            total_cost = (total_tokens / 1_000_000) * plan.monthly_price_usd  # $0.15 per 1M tokens

        return {
            "plan": plan.name,
            "months": months,
            "total_requests": requests_per_month * months,
            "total_tco_usd": round(total_cost, 2),
            "monthly_cost_usd": round(total_cost / months, 2)
        }
    except Exception as e:
        print(f"Cloud TCO error: {str(e)}", file=sys.stderr)
        return {}

def generate_comparison_report(local_tco: Dict, cloud_tco: Dict, requests_per_month: int) -> str:
    """Generate markdown comparison report."""
    report = "# Ollama vs Cloud AI TCO Report\n"
    report += f"**Monthly Requests**: {requests_per_month:,}\n\n"
    report += "## Local Ollama (3-Year TCO)\n"
    report += f"- Hardware: {local_tco['hardware']}\n"
    report += f"- Total 3-Year Cost: ${local_tco['3yr_tco_usd']}\n"
    report += f"- Monthly Cost: ${local_tco['monthly_cost_usd']}\n\n"
    report += "## Cloud AI (3-Year TCO)\n"
    report += f"- Plan: {cloud_tco['plan']}\n"
    report += f"- Total 3-Year Cost: ${cloud_tco['total_tco_usd']}\n"
    report += f"- Monthly Cost: ${cloud_tco['monthly_cost_usd']}\n\n"
    report += "## Delta\n"
    delta = local_tco['monthly_cost_usd'] - cloud_tco['monthly_cost_usd']
    report += f"Local Ollama is ${delta:.2f} more expensive per month than cloud alternative\n"
    return report

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Calculate TCO for local vs cloud AI dev assistants")
    parser.add_argument("--hardware", type=str, default="RTX 4090 24GB", help="Local hardware name")
    parser.add_argument("--cloud-plan", type=str, default="GitHub Copilot Business", help="Cloud plan name")
    parser.add_argument("--monthly-requests", type=int, default=5000, help="Monthly code requests")
    args = parser.parse_args()

    # Find selected hardware
    selected_hardware = next((h for h in LOCAL_HARDWARE_OPTIONS if h.name == args.hardware), None)
    if not selected_hardware:
        print(f"Hardware {args.hardware} not found. Available options: {[h.name for h in LOCAL_HARDWARE_OPTIONS]}", file=sys.stderr)
        sys.exit(1)

    # Find selected cloud plan
    selected_plan = next((p for p in CLOUD_PLANS if p.name == args.cloud_plan), None)
    if not selected_plan:
        print(f"Plan {args.cloud_plan} not found. Available options: {[p.name for p in CLOUD_PLANS]}", file=sys.stderr)
        sys.exit(1)

    # Calculate TCOs
    local_tco = calculate_local_tco(selected_hardware)
    cloud_tco = calculate_cloud_tco(selected_plan, args.monthly_requests)

    if not local_tco or not cloud_tco:
        sys.exit(1)

    # Generate and print report
    report = generate_comparison_report(local_tco, cloud_tco, args.monthly_requests)
    print(report)

    # Save to file
    with open("tco_report.md", 'w') as f:
        f.write(report)
    print("Report saved to tco_report.md")

import os
import sys
import subprocess
import tempfile
from typing import List, Dict, Tuple
from dataclasses import dataclass

@dataclass
class ValidationResult:
    task_id: str
    tool: str
    has_syntax_error: bool
    has_type_error: bool
    passes_tests: bool
    lint_issues: int

def check_syntax(code: str, filename: str = "generated_code.py") -> bool:
    """Check if code has Python syntax errors."""
    try:
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(code)
            temp_path = f.name
        result = subprocess.run(
            [sys.executable, "-m", "py_compile", temp_path],
            capture_output=True,
            text=True
        )
        os.unlink(temp_path)
        return result.returncode != 0
    except Exception as e:
        print(f"Syntax check error: {str(e)}")
        return True  # Assume error if check fails

def check_types(code: str) -> bool:
    """Check for type errors using mypy."""
    try:
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(code)
            temp_path = f.name
        result = subprocess.run(
            ["mypy", temp_path, "--ignore-missing-imports"],
            capture_output=True,
            text=True
        )
        os.unlink(temp_path)
        # Return True if there are type errors (mypy returns 1 if errors)
        return result.returncode == 1
    except FileNotFoundError:
        print("mypy not installed, skipping type check")
        return False
    except Exception as e:
        print(f"Type check error: {str(e)}")
        return False

def run_linter(code: str) -> int:
    """Run pylint and return number of issues."""
    try:
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(code)
            temp_path = f.name
        result = subprocess.run(
            ["pylint", temp_path, "--disable=all", "--enable=error,warning"],
            capture_output=True,
            text=True
        )
        os.unlink(temp_path)
        # Count lines with error/warning
        return len([line for line in result.stdout.split("\n") if "error" in line.lower() or "warning" in line.lower()])
    except FileNotFoundError:
        print("pylint not installed, skipping lint check")
        return 0
    except Exception as e:
        print(f"Lint check error: {str(e)}")
        return 0

def run_test_cases(code: str, test_cases: List[str]) -> bool:
    """Run test cases against generated code."""
    try:
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(code + "\n\n")
            # Add test cases
            for test in test_cases:
                f.write(f"def test_case(): {test}\n")
                f.write("try:\n")
                f.write("    test_case()\n")
                f.write("    print('PASS')\n")
                f.write("except Exception as e:\n")
                f.write("    print(f'FAIL: {e}')\n")
            temp_path = f.name
        result = subprocess.run(
            [sys.executable, temp_path],
            capture_output=True,
            text=True,
            timeout=10
        )
        os.unlink(temp_path)
        # Check if all tests passed
        return "FAIL" not in result.stdout
    except subprocess.TimeoutExpired:
        print("Test case timed out")
        return False
    except Exception as e:
        print(f"Test run error: {str(e)}")
        return False

def validate_generated_code(task_id: str, tool: str, code: str, test_cases: List[str]) -> ValidationResult:
    """Full validation of generated code."""
    has_syntax_error = check_syntax(code)
    has_type_error = check_types(code) if not has_syntax_error else False
    lint_issues = run_linter(code) if not has_syntax_error else 0
    passes_tests = run_test_cases(code, test_cases) if not has_syntax_error else False

    return ValidationResult(
        task_id=task_id,
        tool=tool,
        has_syntax_error=has_syntax_error,
        has_type_error=has_type_error,
        passes_tests=passes_tests,
        lint_issues=lint_issues
    )

if __name__ == "__main__":
    # Example usage with sample generated code
    sample_ollama_code = """
def add_numbers(a, b):
    return a + b
"""
    sample_cloud_code = """
def add_numbers(a: int, b: int) -> int:
    return a + b
"""
    sample_tests = ["assert add_numbers(1,2) == 3", "assert add_numbers(-1, 1) == 0"]

    print("Validating Ollama generated code...")
    ollama_result = validate_generated_code("sample_1", "Ollama 0.5", sample_ollama_code, sample_tests)
    print(f"Ollama Result: Syntax Error: {ollama_result.has_syntax_error}, Type Error: {ollama_result.has_type_error}, Passes Tests: {ollama_result.passes_tests}, Lint Issues: {ollama_result.lint_issues}")

    print("\nValidating Cloud generated code...")
    cloud_result = validate_generated_code("sample_1", "Cloud GPT-4o-mini", sample_cloud_code, sample_tests)
    print(f"Cloud Result: Syntax Error: {cloud_result.has_syntax_error}, Type Error: {cloud_result.has_type_error}, Passes Tests: {cloud_result.passes_tests}, Lint Issues: {cloud_result.lint_issues}")

# /etc/systemd/system/ollama.service
[Unit]
Description=Ollama Local AI Service
After=network.target

[Service]
User=ollama
Group=ollama
Restart=always
RestartSec=5
ExecStart=/usr/local/bin/ollama serve
# Limit CPU/memory if needed
# CPUQuota=200%
# MemoryMax=24G

[Install]
WantedBy=multi-user.target

# .git/hooks/pre-commit
#!/bin/bash
for file in $(git diff --cached --name-only | grep -E '\.py$|\.sql$'); do
  if grep -q "# AI-GENERATED" "$file"; then
    echo "Validating AI-generated file: $file"
    # Run pylint
    pylint "$file" --disable=all --enable=error
    if [ $? -ne 0 ]; then
      echo "AI-generated file $file has pylint errors. Fix before committing."
      exit 1
    fi
    # Run mypy for Python files
    if [[ "$file" == *.py ]]; then
      mypy "$file" --ignore-missing-imports
      if [ $? -ne 0 ]; then
        echo "AI-generated file $file has type errors. Fix before committing."
        exit 1
      fi
    fi
  fi
done
exit 0

echo "scale=2; (10 * 100) / (365 * 24)" | bc  # 10 hours/year * $100/hour / total hours per year = $0.11 per hour effective loss