Your suggestion at the end is a form of "speaking the keyboard" problem that has plagued speech-driven programming for years. It is so wonderful that I can dict

Your suggestion at the end is a form of "speaking the keyboard" problem that has plagued speech-driven programming for years. It is so wonderful that I can dictate the specs and then verbally cut and paste them into the LLM. If I had the energy, I would build an LLM interface that used speech recognition-friendly text areas that one could use speech for editing and revising results.[1] Your response, however, touches on the meta-problem of adding additional information to a training set. For example, as people learn and generate more nim code, the community can expand the LLM capabilities without going through open AI or whoever. I know training requires a lot of GPU time, so training over distributed GPUs is necessary. For example, I would buy one or two GPUs and pay for the electricity to contribute to community LLM training efforts. [1] For anyone interested in helping, it would be a simple two-panel design: top for editing, lower for LLM results, and two buttons, first for "copy to clipboard," second for "submit to LLM."