471–480 of 615 posts
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Right, but using git is a team wide thing. I can’t use perforce while my company is on git. But if I do or do not use an LLM to assist me while coding, my team is unaffected. If someone liked jetbrains, but your team used neovim, would you force them to use neovim?
Editors may also be a team decision in some places. Some teams are using features unique to one IDE, for example.
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The environmental arguments are hilarious to me as a diehard crypto guy. The ultimate answer to “waste” of electricity arguments is that energy is a free market and people pay the price if it’s useful for them. As long as the activity isn’t illegal then training LLMs or mining bitcoins, it doesn’t matter. I pay for the electricity I use.
Do you think that it we should make it illegal to mine coins if the majority of people think the environmental cost is too high?
About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…
Is there a way to use this in Jetbrains IDEs? (I've not been impressed with their AI Assistant.) There are a few plugins, but from the reviews they all seem kind of mediocre.
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I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…
The context here is super-important - the commenter is the author of Redis. So, a super-experienced and productive low-level programmer. It’s not surprising that Staff-plus experts find LLMs much less useful. Though I’d be interested if this was an opinion on “help me write this gnarly C algorithm” or “help me to be productive in ” as I find a big productivity increase from the latter.
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I'm surprised at the description that it's "useless" as a programming / design partner. Even if it doesn't make "elegant" code (whatever that means), it's the difference between an app existing at all, or not. I built and shipped a Swift app to the App Store, currently generating $10,200 in MRR, exclusively using LLMs. I wouldn't describe myself as a programmer, and didn't plan to ever build an app, mostly because in…
The context here is super-important - the commenter is the author of Redis. So, a super-experienced and productive low-level programmer. It’s not surprising that Staff-plus experts find LLMs much less useful. Though I’d be interested if this was an opinion on “help me write this gnarly C algorithm” or “help me to be productive in ” as I find a big productivity increase from the latter.
LLMs are like a pretty smart but overly confident junior engineer, which is what a senior engineer usually has to work with anyway.
An expert actually benefits more from LLMs because they know when they get an answer back that is wrong so they can edit the prompt to maybe get a better answer back. They also have a generally better idea of what to ask. A novice is likely to get back convincing but incorrect answers.
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Quick example. I was implementing dot product between two quantized vectors that have two different min/max quantization ranges (later I changed the implementation to just centered range quantization, thanks to Claude and what I'm writing in this comment). I wanted to still have the math with the integers and adjust for the ranges at the end. Claude was able to mathematically scompose the operations as multiplication…
LLMs being able to detect bugs in my own code is absolutely mind blowing to me. These things are “just” predicting the next token, but somehow are able to take in code that has never been written before and somehow understand it and find what’s wrong with it. I think I’m more amazed by them because I know how they work. They shouldn’t be able to do this, but the fact that they can is absolutely jaw dropping science f…
It is very unreliable at fixing things or writing code for anything non standard. Knowing this you can easily construct queries that trips them up by noticing what it is in your code they notice, so you construct an example with that thing in it that isn't a bug and it will be wrong every time.
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The context here is super-important - the commenter is the author of Redis. So, a super-experienced and productive low-level programmer. It’s not surprising that Staff-plus experts find LLMs much less useful. Though I’d be interested if this was an opinion on “help me write this gnarly C algorithm” or “help me to be productive in ” as I find a big productivity increase from the latter.
Why would the author of Redis describe himself as “not a programmer”? That’s a little odd.
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The context here is super-important - the commenter is the author of Redis. So, a super-experienced and productive low-level programmer. It’s not surprising that Staff-plus experts find LLMs much less useful. Though I’d be interested if this was an opinion on “help me write this gnarly C algorithm” or “help me to be productive in ” as I find a big productivity increase from the latter.
Quick example. I was implementing dot product between two quantized vectors that have two different min/max quantization ranges (later I changed the implementation to just centered range quantization, thanks to Claude and what I'm writing in this comment). I wanted to still have the math with the integers and adjust for the ranges at the end. Claude was able to mathematically scompose the operations as multiplication…
I get this sentiment from a lot of AI startups, that they have a product which can do amazing things, but due to its failure modes makes it almost useless as, to use an analogy from self-driving cars, the users have to still constantly pay attention to the road: you don't get a ride from Baltimore to New York where you can do whatever you please, you get a ride where you're constantly babysitting an autonomous vehicle, bored out of your mind, forced to monitor the road conditions and surrounding vehicles, lest the car make a mistake costing you your life.
To take the analogy farther, after experimenting with not using LLM tools, I feel that the main difference between the two modes of work is similar to driving a car and being driven by an autonomous care: you exert less mental effort, not, you get to your destination faster.
Another point of the analogy are things like Waymo. They really can do a great job of driving autonomously. But, they require a legible system of roads and weather conditions. There are LLM systems too that when given a legible system to work in can do a near perfect job.
Great summary of highlights. Don't agree with all, but I think it's a very sound attempt at a year in review summary >LLM prices crashed This one has me a little spooked. The white knight on this front (DS) has both announced increases and has had staff poached. There is still Gemini free tier which is ofc basically impossible to beat (solid & functionally unlimited/free) but it's google so reluctant to trust. Seriou…
Is it free free? The last time I checked there was a daily request limit, still generous but limiting for some use cases. Isn't it still the case?
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E.g. tax payers.
Are tax payers subsiding that particular activity of Google or Amazon? If they do, “they make enough money” to cover costs. If they don’t, how does it become profitable if it doesn’t even cover the cost of one of the inputs?
This means that they could make a profit off inference models without the revenue being large enough to pay the energy costs.
If it's the case I don't know. I'm more concerned with getting rid of those corporations altogether since interacting with them is generally forbidden due to the lack of data protection regulations in the US.