Main problem with regular (forward-only time) debugging is a state -- memory, CPU, cache etc -- which is contributed to the bug but is completely lost. With time travel debugging that can be saved which is great but now you have a bunch of data that you need to sift through as you trace the bug. Seems like AI is the right tool to save you this drudgery and get to the root cause sooner (or let AI work on it while you do other things in parallel).
This is new. Something that couldn't have been possible without either of time travel debugging or latest AI tech (MCP, code LLMs).
It will be interesting to know what challenges came up in nudging the model to work better with time travel debug data, since this data is novel and the models today might not be well trained for making use of it.
Main problem with regular (forward-only time) debugging is a state -- memory, CPU, cache etc -- which is contributed to the bug but is completely lost. With time travel debugging that can be saved which is great but now you have a bunch of data that you need to sift through as you trace the bug. Seems like AI is the right tool to save you this drudgery and get to the root cause sooner (or let AI work on it while you do other things in parallel).
This is new. Something that couldn't have been possible without either of time travel debugging or latest AI tech (MCP, code LLMs).
It will be interesting to know what challenges came up in nudging the model to work better with time travel debug data, since this data is novel and the models today might not be well trained for making use of it.