- cross-posted to:
- programmerhumor@lemmy.ml
- cross-posted to:
- programmerhumor@lemmy.ml
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Started hating working in tech a year ago and got out.
Developer to LLM: I am your father!
If YouTube’s automated subtitles are anything to go by, it’ll randomly start thinking I’m speaking Vietnamese.
Techbros will have workers do anything but work from home.
Or have single-person offices instead of an open space
I just cannot tell if this is satire or not
Mate they could just type out the prompt instead of dictating with the vader mask.
Welcome to clown world!
Back in the day there used to be specialized equipment for this purpose called an office, of which you had your own and could close the door to
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.
AI needs to measure the level of confidence in your voice, to calibrate its bullshit accordingly
Speaking is faster than typing, I guess?
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.
LLMs don’t need accuracy. This just boils down to speaking being faster than typing, especially if your thought isn’t fully formulated.
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.
We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping.
Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10×), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20×, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs.
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).
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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.
Save money. Get human shaped boxes with lids that they can lay down in to save energy to increase productivity!
Next up: Humans are hardwiring themselves to the computers, with only the brain and a few select “most important” organs remaining of them.
Do people really talk to LLMs that often?
No. Either you do it to make some weird KPI go up or you work on a very simple product.
I think stenographers in courts are already using them.
This is how you get Guild Navigators, you dont want that do you?
Fucking idiotic.
Stick the other end in someone’s butthole and, boom, you got the e-human centipede!
