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IBM Bob$20/monthFREE
Imagine it is your first week as a junior engineer at a German car-parts company, and an old program that decides when cars get serviced is quietly reporting wrong numbers. This is the time to show your skill. Get your own copy of the legacy repository, have IBM Bob read it, then fix and modernize the code with it, catching the mistakes it makes. Verify with the repo's own acceptance script and hand back your public GitHub link as proof.
Just completed
A graded ProoV project, completed step by step and marked against a published rubric.
Your first week at a car-parts company. Meet KM-Waechter, the small old service that decides when each car gets serviced, and the car it quietly let through.
Take the broken repository, spot the line producing the wrong wear percent, and explain in your own words why it reports 0 percent for a car that is nearly due.
Choose a prompt that bounds what the agent may change, then fix and modernize the whole repository with an AI coding agent and add a breakdown-risk analysis over the fleet history.
Review what the agent produced, catch the mistakes it planted, say why each one must not ship, and write the acceptance checklist the fix has to pass.
Run the repository's acceptance script, push to your own public GitHub, hand back the link, and explain what broke and what the AI got wrong in plain language a non-engineer could follow.
A car sits at 14,900 of its 15,000 km service interval and the service reports 0 percent worn. Whole-number division floors the wear, so the car is never flagged and it breaks down on the road. You find the line and explain why.
You scope the prompt, let the agent do the work, and hold the rules it must not touch: the 15,000 km interval and the 80 percent threshold stay exactly as they are.
You review a version of the agent's work and find what it actually did: it brought the rounding back, it moved a threshold nobody asked it to move, and it shipped no test proving the fix. You write the checklist it has to pass before you accept it.
A history of 120 labelled cars. Total mileage and age do not predict a breakdown; the gap since the last service and how hard the car is driven do. You rank the cars by risk and follow what the data says, not what everyone assumes.
The fixed code, the missing test, the risk analysis, and NOTES.md written in your own words about what the agent got wrong. You push it to your own public GitHub and hand back the link. The real code is what gets graded.
The experince of working on legacy code and how it is used as template to advancement is clearly the most setting apart experience of it.
I learned how a small bug like // instead of / can delete 3000 km of safety warning and how to use an AI agent like Bob while still checking its work and keeping core rules like 15000 km and 80% threshold untouched.
This project taught me how to use AI-assisted tools to understand, modernize, and debug existing code more efficiently. I also learned that AI suggestions should always be reviewed and tested to make sure the original logic is preserved.
This project taught me how to use an AI coding agent effectively instead of blindly trusting it. I learned to understand the code, spot mistakes, test the changes, and make sure the final result actually matches the requirements.
I learned to check an AI agent’s work, not just accept it. I also learned that tests and verify.py matter more than the agent saying it is done.
I gained real confidence in debugging and verifying code. The project taught me how to spot subtle mistakes, prove my fixes with tests, and explain the impact in plain language — skills that matter both for software quality and for teamwork.
