In a perfect world, every change made by an autonomous coding agent is tracked remotely and isolated from the work of other agents.
Even with ‘regular’ Claude Code, making changes from a couple terminal tabs on a single project gets hectic. I found myself making smorgasbord commits like ‘recent stuff’ containing scattered changes from sessions over the past hour. Once scaled up to 5-10 agents working on separate features, isolation becomes a necessity.
1. Linear Issues I spent years building scaffolding for coding agents. Custom prompts, state machines, retry logic, context management, most of it quicky became overhead that the models buckled under.
Then they got trained on tool-use traces, and suddenly the patterns were just there: when to call functions, how to recover from errors, when to retry versus give up. The same infrastructure that used to weigh them down became force multiplication.
github pypi phase prediction per best epoch
phasefinder is a beat estimation model that predicts metric position as rotational phase, heavily inspired by this paper. Demos Your browser does not support the audio element. `are you looking up`
mk.gee Your browser does not support the audio element. `just the way it is`
action bronson Your browser does not support the audio element. `lethal weapon`
github pypi DeepRhythm is a convolutional neural network designed for rapid, precise tempo prediction for modern music. It runs on anything that supports Pytorch (I’ve tested Ubuntu, MacOS, Windows).
Audio is batch-processed using a vectorized harmonic constant-q modulation (HCQM), drastically reducing computation time by avoiding the usual bottlenecks encountered in feature extraction.
Why? I needed a way to accurately estimate tempo that was small & fast enough run on a Raspberry Pi.