This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine.
Every codebase leaves a trace. Some refuse to disappear. 👻
What I Built
Meet The Last Commit — a haunted code archaeologist that uses open-weight AI to help developers understand forgotten code, investigate potential bugs, and breathe new life into legacy snippets.
The idea came from a familiar developer experience: opening an old file, discovering a strange function, and wondering why anyone wrote it that way in the first place.
Instead of treating legacy code analysis like another ordinary chatbot interaction, I wanted to turn it into an investigation inside a mysterious, retro-futuristic computer system.
The result combines practical AI-powered code analysis with a cinematic horror atmosphere inspired by haunted CRT terminals, digital ghosts, and the feeling that an old program still has something to say.
Three ways to investigate forgotten code
🕯️ Séance Mode
Summon the fictional spirit of a code snippet. It narrates what the code used to do while providing a grounded technical explanation of its behaviour, assumptions, and missing context.
🩸 Autopsy Mode
Investigate potential bugs, deprecated patterns, security concerns, error-handling gaps, and other maintenance risks. Findings are presented with explanations and suggested next steps, rather than simply declaring code broken.
⚡ Resurrection Mode
Give an old snippet a fresh start. Generate a proposed modernized version, understand the changes, and review compatibility caveats and suggested tests before using it.
The Forgotten System — a playable Matrix terminal
The experience extends beyond the code-analysis workspace.
The Forgotten System features an interactive Matrix-style terminal with falling characters, pointer-reactive ripples, spectral signals, and a Code Graveyard where developers can discover old code fragments and send them into the AI workspace for further analysis.
The terminal includes configurable animation performance and optional retro-computer ambience, creating an interactive Halloween atmosphere without making the developer tools themselves unusable.
A personal archive
The Archival Vault stores selected analysis sessions in the browser, letting developers revisit previous investigations, search saved records, copy reports, and download Markdown artefacts.
The project never executes submitted code. It analyses the snippet as text, keeping the experience focused on explanation and static inspection.
Demo
👻 Enter The Last Commit — Live Demo
Explore the haunted terminal, investigate a code snippet, and see what the ghost has to say.
Code
💻 Explore the source code on GitHub
The repository includes the application source, server-side model integration, tests, configuration examples, and setup documentation.
How I Built It
I built The Last Commit using Google AI Studio Build mode, React, TypeScript, Vite, Tailwind CSS, Node.js, Express, and the official @google/genai SDK.
The AI component uses Google's open-weight Gemma 4 model, configured as gemma-4-26b-a4b-it.
The application sends code snippets to a server-side endpoint, which requests structured analysis from the configured model. The frontend then presents the response in one of the three analysis modes.
I kept the interactive Matrix renderer separate from the AI service. The falling characters, pointer interactions, visual effects, and animation controls run in the browser; they do not require an AI request for every frame.
Saved investigations use browser localStorage, so a remote database or user account is not required for the current version.
Why Does Open Innovation Matter?
A tool that analyses code should not have to be permanently tied to one closed model or one fixed way of interpreting software.
Using an open-weight model gives developers the opportunity to inspect the published model and its applicable licence, experiment with prompts and analysis workflows, and explore alternative inference environments as the project evolves.
Gemma is central to the analysis rather than an optional chatbot added to the interface. It provides the reasoning and language-generation component behind the explanations, potential findings, and modernization suggestions.
There is an important distinction in the current implementation: The Last Commit uses hosted inference through the Gemini API. Submitted source code is sent to the model provider for analysis, and the current deployment is not an entirely offline application. Saved reports remain in browser storage, but this does not mean inference happens locally.
The separation between the model service, the browser interface, and the local archive leaves room to experiment with self-hosted inference in environments where keeping source code on a local machine is a requirement.
For me, open innovation is not just about using a different model. It is about having room to understand, adapt, and eventually change the technology behind the tools we build.
The Spirit of It 👻
Halloween seemed like the perfect excuse to give an ordinary developer problem a slightly unsettling personality.
Old code often feels haunted: unexplained decisions, forgotten dependencies, strange behaviours, and functions nobody remembers writing. The Last Commit turns that feeling into an interactive experience while keeping the underlying goal practical.
The ghost is fictional. The code investigation is real work for the AI to assist with.
This is an early version, and I intend to keep improving it beyond Hacktoberfest — exploring better analysis, more useful developer workflows, and alternative ways to run open-weight models.
Every codebase leaves a trace. This one just happens to whisper back.
Top comments (0)