This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
We've all done it: start a project with a burst of enthusiasm, get busy with something else, and return weeks or months later to a repository that feels like someone else's code.
You know there's something worth finishing in there. But before you can make progress, you have to reconstruct the context. What was already implemented? Which parts were unfinished? What was that promising idea you wanted to explore? And where do you even begin?
Reboot AI helps you pick up where you left off.
Give it the path to a local Git repository, and Reboot inspects the README, files, recent commits, working-tree status, TODOs, and selected source-code excerpts. It builds a picture of the project's current state, searches arXiv for potentially relevant research, and uses Gemma to turn the available evidence into a practical project revival report.
Instead of merely saying "here are some things you could improve," the goal is to connect recommendations to actual files, functions, research papers, and concrete implementation steps.
The research component is important. A forgotten project might not need another generic list of coding suggestions; it might need a paper that explains a better algorithm, a missing benchmark, or a technique the original author never got around to implementing.
For example, I tested Reboot on a Poker-Bot repository built around Counterfactual Regret Minimization (CFR), Bayesian opponent modelling, and exploitation in imperfect-information games. Its report identified empty Leduc poker and Deep CFR modules, highlighted limitations in the opponent-model evaluation, and suggested relevant research directions with implementation steps.
That is the kind of comeback I want Reboot to make easier.
The people who benefit are students with abandoned research prototypes, developers returning to side projects, and anyone whose enthusiasm for a project outlasted their available time.
The ambition is simple: less time remembering what you were doing, more time actually doing it.
Demo
Project walkthrough:
The demo shows Reboot inspecting the Poker-Bot repository, identifying unfinished components, retrieving relevant arXiv papers, and generating a project-specific restart plan.
Repository:
theikosB
/
reboot-ai
Revive forgotten projects with open-source AI, persistent project memory, and evidence-backed research. For submission to Hacktoberfest 2026 Week 3 Challenge: Slow and Steady
Reboot AI
Give forgotten projects a second wind.
Reboot AI helps developers return to unfinished or abandoned code projects without spending hours reconstructing what they were building Point it at a local Git repository and it builds a project report searches arXiv for potentially relevant research, and uses the repository evidence and retrieved papers to suggest a practical restart plan.
Project status: Early-stage prototype. Research matches and AI-generated recommendations are leads to verify, not guaranteed fixes.
Why Reboot?
Returning to an old repository often means rereading the README, scanning files and commit history, hunting for TODOs, and trying to remember which ideas were worth pursuing. Reboot brings these clues together in one report and suggests concrete next steps.
The goal is not to write the entire project for you. It is to reduce the friction of getting started again.
What it does
- Inspects a local repository: gathers the README, file…
Code
The project is built with Python and Flask, with separate modules for repository inspection, arXiv research retrieval, and AI-assisted synthesis.
The main components are:
-
app.py— Flask application and web interface routes. -
reboot/workspace.py— gathers repository evidence. -
reboot/arxiv_search.py— searches arXiv and attempts limited PDF text extraction. -
reboot/agent.py— derives research queries, ranks candidate papers, and generates the restart plan using Gemma. -
tests/test_research.py— initial automated tests for paper ranking, deduplication, and PDF extraction failures.
Source code: https://github.com/theikosB/reboot-ai
How I Built It
I built Reboot AI in Python, using Flask for the interface and Google's GenAI SDK to access Gemma. The research pipeline retrieves papers from arXiv and ranks candidates using repository-derived queries and a heuristic relevance score.
The application has three main stages.
1. Recover the project's context.
Reboot inspects the repository for evidence of what exists and what remains unfinished. It gathers the README, file inventory, recent commits, working-tree information, TODO/FIXME markers, and selected source excerpts.
2. Find relevant research.
Rather than making the user manually search for papers, Reboot derives a small number of research queries from the repository itself. It searches arXiv, removes duplicate results, ranks candidate papers, and attempts to extract text from the opening pages of a limited number of PDFs.
3. Turn the evidence into a restart plan.
Gemma receives the repository evidence and retrieved research material and is prompted to produce a Markdown report covering the project's current state, research-backed opportunities, concrete implementation steps, and limitations. The agent is instructed to distinguish observations from inferences and not to invent research findings or pretend that a partial PDF excerpt represents a complete paper review.
I also added a fallback path so that a model failure does not automatically discard the repository analysis and retrieved paper information.
The current model configuration is gemma-4-26b-a4b-it. I verified that a direct API request to this model returns a response; the complete restart-plan workflow still needs to be verified against the same configuration.
Why Does Open Innovation Matter?
Reboot is a project about helping people continue work that matters to them. I wanted its AI component to be built around an open-weight model rather than treating the model as an interchangeable black box.
Gemma gives the project a model whose weights are available under Google's applicable terms, creating room to explore different deployment arrangements and, potentially, future local inference or model customization. That matters for a tool that reasons over people's source code and unfinished research projects.
There is an important limitation in the current implementation: Reboot calls Gemma through Google's hosted API. It does not currently run the model locally. Repository content sent for synthesis therefore follows the configured provider's data-handling terms. I am not claiming that the current version works offline or keeps all source code on the user's machine.
The value of open innovation is the flexibility it creates for the next iteration. Reboot can investigate different serving options, compare models on the same repository-revival task, and eventually explore local inference for users who cannot send private code to a hosted service.
For this challenge, I am using Gemma as an actual part of the application's synthesis pipeline, not simply mentioning it in the README. The next step is to validate the complete workflow and document its performance and limitations honestly.
The Lazy Payoff
The time Reboot is designed to save is the time spent reconstructing context before any meaningful work can resume.
A returning developer would otherwise have to read the README, browse source files, inspect the commit history, search for unfinished work, and manually investigate research directions. Reboot brings those steps into one report and provides a shortlist of possible next actions.
I have demonstrated the workflow on a real Poker-Bot repository, but I have not yet measured the time saved against a manual baseline. I would rather report a measured result after testing than invent an impressive number.
My intended payoff is straightforward: instead of spending an evening remembering how a project works, spend that evening making the first meaningful change.
Prize Categories
- Best Use of Gemma — Reboot uses Google's Gemma model for AI-assisted project analysis and restart-plan synthesis.
What's Next?
Before calling Reboot finished, I want to verify the complete restart-plan workflow with Gemma, expand end-to-end tests, improve error handling, and make the evidence behind each recommendation easier to inspect.
The long-term goal is to make returning to an unfinished project feel less like starting over and more like continuing a conversation with your past self.
Because sometimes the hardest part of finishing a project isn't the code. It's remembering why you started.
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