This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Have you ever reopened a project after a few days away and spent the first 20 minutes trying to remember what you were doing?
Which files changed? What did the last commits accomplish? What still needs attention? Where should you start?
I built ContextSwitch, a read-only project-resumption assistant designed to answer those questions.
Instead of manually piecing together repository history, ContextSwitch gathers Git commits, working-tree changes, documentation, and TODO-style markers into a structured report. It then suggests possible next steps, with an optional AI-generated briefing to make the information easier to digest.
The goal is simple: spend less energy remembering where you left off and more energy actually building.
Demo
Code
GitHub repository: https://github.com/sanvinaik/ContextSwitch
The project is written in Python and uses Git as its primary source of repository evidence.
How I Built It
ContextSwitch combines deterministic repository inspection with optional AI summarization.
The workflow collects:
Recent commits and repository identity.
Staged, unstaged, untracked, and deleted paths where available.
Readable file diffs.
Selected project documentation.
TODO, FIXME, HACK, XXX, and BUG markers from a bounded file scan.
Suggested next actions and an evidence register.
For AI-powered briefings, ContextSwitch uses Groq with the open-weight openai/gpt-oss-20b model. A no-AI mode is also available.
I wanted the tool to help developers regain context without taking control away from them. It does not intentionally modify the target repository or execute its project code.
Why Does Open Innovation Matter?
A developer's project context can contain sensitive information, so transparency and control matter.
Using an open-weight model gives the AI component a model choice that can be evaluated and potentially replaced with another compatible model or deployment. It also makes the model choice more transparent than relying on an opaque, fixed assistant.
There is an important trade-off in the current implementation: AI mode sends selected repository context to the configured Groq service. The no-AI mode avoids that AI-provider transfer and relies on local inspection and deterministic report generation.
Open innovation matters because developers should be able to inspect, adapt, and evolve the tools they depend on rather than being locked into a single closed workflow.
The Lazy Payoff
The idea is to turn the familiar “Where was I?” moment into a report that gives you somewhere concrete to start.
Rather than manually checking commits, searching for unfinished work, and reopening documentation one file at a time, you get those signals in one place.
ContextSwitch is an early version, so I am not claiming a measured time saving yet. Its purpose is to reduce the repetitive mental overhead of returning to interrupted work.
Prize Categories
Slow and Steady
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