# 👻 PHANTOM: The AI That Haunts Your Terminal > Source: > Published: 2026-10-29 15:09:38+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine](https://dev.to/challenges/hacktoberfest-week4-2026-10-26)* What if your terminal had a ghost that could help you investigate suspicious code? Meet **PHANTOM**, a local-first AI coding and security investigator built to help developers uncover potential security issues and understand what to investigate next. Security scanners can flag suspicious patterns, but understanding the context and deciding what to do about each finding can still take time. PHANTOM combines deterministic security scanning with locally running AI models to make that investigation process more accessible. I interpreted the **Ghost in the Machine** theme as an intelligent presence inside the developer's environment: something that quietly examines code, surfaces hidden risks, and helps explain what might be going wrong. The goal isn't to replace security professionals or claim that AI can find every vulnerability. PHANTOM combines predictable scanning logic with AI-assisted explanations so developers can investigate potential risks more effectively. 🎥 **Watch PHANTOM in action:** [▶️ Watch the PHANTOM demo video](https://drive.google.com/file/d/1TtAtsLQvHBBelNzTngE2q4feAnFAzcA-/view?usp=sharing) The demo video showcases the project and its workflow. 💻 **GitHub repository:** [msairitvik/phantom](https://github.com/msairitvik/phantom) PHANTOM is built with an open-source technology stack: Explore the source code, review the implementation, and contribute ideas or improvements through the repository. I built PHANTOM by connecting a React and TypeScript frontend to a Python FastAPI backend, with Ollama providing access to locally running language models. The application follows a workflow: I experimented with locally available models, including `gemma2:2b`, `qwen3:4b`, and `qwen3:0.6b`, through Ollama. One important engineering challenge was balancing model capability with response time. Smaller models can be useful for local workflows, but they have limited context and may produce inconsistent responses. I worked on relevance filtering, context limits, timeout handling, and request management to make the investigation experience more practical. The backend test suite passed **20 tests** during development. The AI is an assistant to the scanning process, not a replacement for deterministic checks. That distinction matters because AI-generated explanations can be incomplete or incorrect. Open innovation made it possible to build PHANTOM using tools and models that developers can inspect, run, experiment with, and adapt. Ollama and locally available open-weight models let me explore AI-assisted security investigation without making a hosted AI API a mandatory part of the core inference workflow. This approach offers several advantages: Local-first doesn't automatically guarantee privacy or perfect security. File access, logs, configuration, and the application's own behavior still matter. But open tools give developers greater control over how they build and evaluate these systems. PHANTOM's development involved iterating on the application, investigating timeout issues, improving model-response handling, and validating changes through automated tests. I also used development tools to refine the implementation and verify that the backend test suite continued to pass. **Project repository:** [https://github.com/msairitvik/phantom](https://github.com/msairitvik/phantom) *Agent session recording: Add your DevRelay session link or embed here if you have saved and shared one. Otherwise, this optional section can be removed.* The most interesting part of PHANTOM was exploring how a small local AI model could become part of a practical developer workflow rather than just another chatbot. A suspicious line of code is only the beginning. The more useful question is: *Why might this be dangerous, what context matters, and what should I investigate next?* That is the idea behind PHANTOM's ghost: a quiet investigator inside the development environment, helping bring hidden risks into view. Building this project also reinforced an important lesson: making an AI application useful involves more than connecting a model. Reliability, context management, testing, privacy-conscious design, and clear explanations all matter. *Select only the partner prize categories that PHANTOM actually qualifies for under the official challenge rules.* Potentially relevant categories to check: I have not verified the current partner-category list, so confirm the exact category names on the [official challenge page](https://dev.to/challenges/hacktoberfest-week4-2026-10-26) before entering them. **Built for the Hacktoberfest 2026 Open-Source AI Challenge — Week 4: Ghost in the Machine.** 👻 **GitHub:** [https://github.com/msairitvik/phantom](https://github.com/msairitvik/phantom) 🎥 **Demo:** [https://drive.google.com/file/d/1TtAtsLQvHBBelNzTngE2q4feAnFAzcA-/view?usp=sharing](https://drive.google.com/file/d/1TtAtsLQvHBBelNzTngE2q4feAnFAzcA-/view?usp=sharing)