Skip to main content

On This Page

Building PC Workman: A Local AI System Monitor in Python

2 min read
Share

These articles are AI-generated summaries. Please check the original sources for full details.

I Built an AI-Powered PC Monitor in Python

Marcin Firmuga built PC Workman, an open-source AI-powered PC resource monitor developed over 10 months. The system consists of 96 Python files and a local hybrid engine that avoids external APIs for data privacy.

Why This Matters

Most monitoring tools are either outdated or rely on cloud APIs that compromise privacy; PC Workman demonstrates a hybrid local architecture where deterministic rule dispatching handles critical system data while LLMs handle natural language, eliminating the risk of AI hallucinations regarding hardware specs.

Key Insights

  • Deterministic vs LLM Logic: The hck_GPT engine uses an 82-intent parser for real data responses and falls back to a local Ollama LLM only when intents are unrecognized (PC Workman 1.7.6).
  • Unicode Normalization Edge Cases: NFD normalization fails to decompose the Polish character ‘ł’ (U+0142), requiring explicit ASCII fallback patterns to ensure intent matching accuracy.
  • Proactive Monitoring Constraints: System alerts are limited by a SESSION_BUDGET of 3 unsolicited alerts per 30 minutes, based on CHI 2025 research to prevent user annoyance.

Working Examples

Normalization function used to strip diacritics for intent matching.

def _ascii_fold(self, text: str) -> str:
    import unicodedata
    return "".join(
        c for c in unicodedata.normalize("NFD", text)
        if unicodedata.category(c) != "Mn"
    )

Simple bilingual helper for language switching.

def _t(lang: str, pl: str, en: str) -> str:
    return en if lang == "en" else pl

Practical Applications

  • System Resource Analysis: Using psutil and WMI to determine if specific hardware requirements (RAM/VRAM/Disk) are met for gaming without hallucination.
  • Proactive Hardware Alerts: Monitoring CPU throttle ratios (below 60% of max frequency) to trigger heat-related warnings automatically.

References:

Continue reading

Next article

Streamlining Docker Swarm and Compose Deployments via GitHub Actions

Related Content