I Use AI 100 Times per Hour(tomtunguz.com)
tomtunguz.com
I Use AI 100 Times per Hour
https://tomtunguz.com/ai-use-in-one-afternoon/
65 comments
> I can generate several hundred lines of code in 5-10 minutes. With the newer models, I expect this to collapse to 1-2 minutes.
Is this supposed to be a good thing?
Is this supposed to be a good thing?
I prob use it 100 times per hour in Cursor AI... if not more.
OtomotO(4)
I don't understand what he's using it for. I'm using it maybe a few times on a busy day, and half of those fail to achieve anything useful.
looks like he's using it for poorly written blog posts about AI
He's using it for text entry (hence all the mentions of dictation).
I know there's no precise technical definition, but is text-to-speech considered "AI"? We've had this stuff since Dragon Naturally Speaking.
I mean, at the end of the day it's all flavours of machine learning ¯\_(ツ)_/¯
Psht, my AI uses AI 1 million times an hour. Slacker!
Why is a usual STT (speech-to-text) called AI?. (fixed typo)
STT (driven by machine learning models) has been part of the academic research field called Artificial Intelligence for decades.
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Probably because AI is a misnomer. Machine learning is a better name, but even than it is pretty lacking.
What is generally called AI in common speech is pretty much a class of non-linear statistical models which require some training to generate weights which are than used to fit the model. Most people that know anything about statistics knows this, so it is fine actually. Misnomers exists in all industries and all science, and we just deal with them.
What is generally called AI in common speech is pretty much a class of non-linear statistical models which require some training to generate weights which are than used to fit the model. Most people that know anything about statistics knows this, so it is fine actually. Misnomers exists in all industries and all science, and we just deal with them.
I believe this article is about STT, not TTS
AI does a better job of STT than the other techs.
"I tracked my Sunday work day" - that's all I needed to skip this one lol.
You have no idea how many times you use it.
Maps? Recommendation systems (the algo)? Other optimizations?
BTW, to the best of my knowledge, dictation still has a high error rate.
I used to work correcting AI transcriptions.
There is only so much AI can do because it currently lacks in certain domain knowledge.
The worst one I ever had to fix was ESPN captions of commentary for some indoor motorcross thing with dirt bikes going around a track. First, the motorbike noise, but secondly, the commentators were using the (well known to fans) nicknames for all the riders, which the AI had no idea how to transcribe, no idea who they were, and were almost impossible for me to even Google.
There is only so much AI can do because it currently lacks in certain domain knowledge.
The worst one I ever had to fix was ESPN captions of commentary for some indoor motorcross thing with dirt bikes going around a track. First, the motorbike noise, but secondly, the commentators were using the (well known to fans) nicknames for all the riders, which the AI had no idea how to transcribe, no idea who they were, and were almost impossible for me to even Google.
Whisper has a feature where you can provide it with a text prompt before it starts transcribing to clue it up to additional vocabulary that it may need to know.
Like it or not "AI" has become synonymous with generative AI, when most people talk about how much they use AI they don't mean the YouTube algorithm or the neural network that handles autocorrect on their phone.
Whisper (OpenAI 2022) and other recent dictation models are rather good. Whisper automatically punctuates sentences and usually gets just a few words wrong in my experience.
A few words per...?
It depends on the audio quality.
Artificial Analysis says Whisper 3 has a 10% word error rate, although it typically does better than that in my experience. When I use Whisper 2 in the ChatGPT app it usually gets 2 or 3 words wrong per paragraph of prompt.
Artificial Analysis says Whisper 3 has a 10% word error rate, although it typically does better than that in my experience. When I use Whisper 2 in the ChatGPT app it usually gets 2 or 3 words wrong per paragraph of prompt.
But that is really bad, no? I am not a user so please enlighten me on the ergonomics, but I understand that to mean I have to check and correct everything.
https://artificialanalysis.ai/speech-to-text/models/deepgram
Below 30% for all modern models.
Below 30% for all modern models.
Is this really interesting to anyone? Asking seriously
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vouaobrasil(6)
I'm probably at 20 times a day, at least for random trivia. What would be great (and maybe it already exists) is a daily email report, formatted for Anki, that helps me remember the things I've asked ChatGPT.
Perplexity has replaced Google Search for me in recent days. Not even comparable.
I've been thinking that a lot lately - LLMs are really nice search engines, which is great, that's a product everyone needs.
Before Google, information is hard to find. After Google, websites are incentivized to bury information behind ads and bullshit. After LLMs, the information is juiced out like lemonade.
I wonder what the next step in the incentive landscape is?
Before Google, information is hard to find. After Google, websites are incentivized to bury information behind ads and bullshit. After LLMs, the information is juiced out like lemonade.
I wonder what the next step in the incentive landscape is?
Once every 36 seconds? Even for simple transcription, that seems insane. This reads like the hustle culture memes...
2:00am wake up
2:05am cold shower
2:15am breakfast: almonds, breast milk bought off facebook, 50mg adderal
2:30am begin transcribing thoughts into the machineI read the whole article, and It could be bragging
or a cry for help.
In any case its clear that the world of performance metrics has achived some kind of unimaginable gravity and angular momentum, and has started to orbit itself
snakeyjake(3)
> employing AI roughly 50-100 times per hour
> Within the last 24 months it’s clear that AI has become an essential coworker
I'm curious why the personification of these tools? Like, with the same logic we could call our dishwashers and washing machines employees and co-workers.
My guess is it is some form of hype-speak to raise the level of perceived importance of the technology for financial gain.
> Within the last 24 months it’s clear that AI has become an essential coworker
I'm curious why the personification of these tools? Like, with the same logic we could call our dishwashers and washing machines employees and co-workers.
My guess is it is some form of hype-speak to raise the level of perceived importance of the technology for financial gain.
Because you don't start your dishwasher by saying to it "alright, I need you to wash my dishes. they are going to probably need some extra soaking because I burned dinner yesterday." Instead, you push the pots and pans button and off to work the robot goes.
The lion's share of people I mute on Twitter are VCs.
Learning to leverage AI as it exists today takes effort, but is usually worth it.
However, ignoring the effort undercuts the distance these products have to go. Speech is great because there is just a single interface to integrate with. Obviously I'm biased to my employer's speech product, but I'm sure there are many.
Biggest thing for me was when I saw that the lem editor[0] posted on hacker news[1] was a small editor, which has 3 top level features: common lisp API, LSP support, and copilot support.
I've installed gptel[2] in emacs, and hacking up a few tools that really make it shine. Up next is figuring out voice + AI + emacs :)
[0]: https://lem-project.github.io/
[1]: https://news.ycombinator.com/item?id=41357409
[2]: https://github.com/karthink/gptel
However, ignoring the effort undercuts the distance these products have to go. Speech is great because there is just a single interface to integrate with. Obviously I'm biased to my employer's speech product, but I'm sure there are many.
Biggest thing for me was when I saw that the lem editor[0] posted on hacker news[1] was a small editor, which has 3 top level features: common lisp API, LSP support, and copilot support.
I've installed gptel[2] in emacs, and hacking up a few tools that really make it shine. Up next is figuring out voice + AI + emacs :)
[0]: https://lem-project.github.io/
[1]: https://news.ycombinator.com/item?id=41357409
[2]: https://github.com/karthink/gptel
"I let my brain atrophy at an accellerated rate."
> In fact, I find myself growing reliant on the AI to the extent that I no longer remember some of the R syntax, A sign of working at a higher level of abstraction : one of the promises of AI.
Polar opposite of what I want to happen to me.
There's joy in knowing one's tools.
Polar opposite of what I want to happen to me.
There's joy in knowing one's tools.
> A sign of working at a higher level of abstraction.
Working at a higher level of abstraction while loosing knowledge at the lower level also means by some degree that one is going to be reliant on the abstraction without any understanding what is abstracted away.
Working at a higher level of abstraction while loosing knowledge at the lower level also means by some degree that one is going to be reliant on the abstraction without any understanding what is abstracted away.
Hustle culture nonsense aside, I find using whisper (AI) transcription instead of typing to be super efficient.
My workflow involves setting up a whisper server, downloading the Whispering(1) app on my computer, and binding it to a shortcut on my keyboard and mouse. Whenever I want to write something down, I just hit the shortcut, dictate and it transcribes instantly. With a Nvidia GPU (1070 in my case), transcription is nearly instantaneous. Although I haven’t set it up on my MacBook yet, I suspect it will be just as fast with Apple Silicon
(1) https://github.com/braden-w/whispering/
You can also use an API like grok, but I'm generally wary of such services.
I'm a bit of an introvert, so I found talking out loud to be awkward at first. But now I can't go back to regular typing, given the efficiency gains.
My workflow involves setting up a whisper server, downloading the Whispering(1) app on my computer, and binding it to a shortcut on my keyboard and mouse. Whenever I want to write something down, I just hit the shortcut, dictate and it transcribes instantly. With a Nvidia GPU (1070 in my case), transcription is nearly instantaneous. Although I haven’t set it up on my MacBook yet, I suspect it will be just as fast with Apple Silicon
(1) https://github.com/braden-w/whispering/
You can also use an API like grok, but I'm generally wary of such services.
I'm a bit of an introvert, so I found talking out loud to be awkward at first. But now I can't go back to regular typing, given the efficiency gains.
> I can generate several hundred lines of code in 5-10 minutes. With the newer models, I expect this to collapse to 1-2 minutes.
It seems main usage is to make plots with R... Which is funny as the bar chart on the page can be done in Excel/Numbers/Sheets by entering a few numbers and headers.