Training a Custom Autocorrect Model with Zero ML Background
GCWebDesigner · x · 2026-07-16
The author shares an experiment that felt like a glimpse of AGI: someone with virtually no machine learning background built their own autocorrect model using GPT-5.6 Sol and Codex /goal.
The core methodology involved breaking the experiment down into iterative attempts, logging results, discarding failures, and planning the next step—essentially automating the R&D process. Remarkably, the custom-trained model outperformed GPT-5.6 Sol on autocorrect tasks, and the entire process cost almost nothing.
The motivation was simple: frustrated by how AI affected their typing habits, the author wanted a fast, energy-efficient, and lightweight local model to fix typos without interrupting the typing flow.
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