An Open-Source Tool Called 'Get-Shit-Done' Tackles One of the Most Persistent Problems in AI Coding: Context Rot
A new open-source framework uses parallel agents to prevent the degradation of context that plagues long-running LLM sessions — and the approach may signal a broader architectural shift in how developers build with AI.
One of the most frustrating failure modes in production AI work has a name among developers: context rot. It's what happens when a language model, midway through a complex multi-step task, begins to lose track of earlier instructions, forget established constraints, or subtly drift from the original goal. The problem is well-documented but poorly solved. Now an open-source project called Get-Shit-Done is taking a novel approach, as first surfaced by @simplifyinAI: instead of fighting context degradation within a single session, it sidesteps the issue entirely by orchestrating parallel agents.
The architecture is straightforward in concept but ambitious in execution. Rather than feeding an ever-growing context window to a single model instance, Get-Shit-Done decomposes tasks into discrete units and assigns them to separate agent threads. Each agent operates with a focused, manageable context. A coordination layer synthesizes their outputs, effectively giving the system the long-horizon coherence that single-session models struggle to maintain.
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