this post was submitted on 13 Jul 2026
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Isn’t it cheaper and quieter to just type out your prompts?
This is akin to people who have conversations on speakerphone in public places.
Maybe this was designed for illiterate coders.
you assume that vibe-coders can actually touch type. or type at all.
I assumed they were all working from their iphones
Or be capable of critical thinking.
AI needs to measure the level of confidence in your voice, to calibrate its bullshit accordingly
I wonder if anyone owns a patent on this idea.
Older people such as myself tend to hate voice-to-text I think because it was so awful in the past. And if you screwed up with it in the past it was a less understandable excuse that “I was using voice-to-text.” And because we were all forced in some way to learn to type well.
Voice to text works a little bit better now. And I think younger people know everyone else uses it and to forgive when it screws up.
Voice to text is better for you now than it was in the past? I remember in 2014 voice to text worked great for me. Now it hardly ever works.
It worked better five years ago before they replaced the existing algorithms with AI bullshit. Keeps adding slurs to my dictionary too since they replaced keyboard prediction with modern AI and so many people use slurs Google just assumes I do too.
I hate it because it’s generally intensely US-centric. Not understanding non-US accents or terms.
Speaking is faster than typing, I guess?
Maybe for Eminem.
apparently audio and images are more efficient compared to text for multimodal models?
I'm going to need significant levels of convincing. Computers have always preferred specificity and accuracy, it's half the reason I'm in my current position (MSP Escalations/level 3, half of my success at fixing issues is being extremely specific in looking up exact error messages instead of paraphrasing).
This isn't a defense of AI; on the contrary, it's my doubt that AI can read intentions/inflection/emotion better than just writing out what you actually want.
Deepseek recently published a paper in which they describe that vision tokens contain more information than text tokens and that this can be used to compress context.
It reminds me of LLM caveman speak, it used to have another option to use Chinese instead of English. A language like Chinese is seemingly better at encoding information in fewer tokens and I think this is the same mechanism why OCR tokens work so well.
That said, I also doubt that voice messages are more efficient than text prompts, but it's best not to waste too much time engaging with these sorts of LinkedIn posts (and LinkedIn in general).
LLMs don't need accuracy. This just boils down to speaking being faster than typing, especially if your thought isn't fully formulated.
As far as I know, these workflows typically involve a transcription model to convert the audio to text, and then passing the text to the model.