261–270 of 581 posts
This jumped out at me: "The NYT claims it discussed OpenAI's negotiations with nine people close to the discussions but who wish to remain anonymous." "Claims"? Generally newspapers do not lie about the sourcing for a story.
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I can't understand how anyone can use these tools (copilot especially) to make entire projects from scratch and expand them later. They just lead you down the wrong path 90% of the time. Personally I much prefer Chatgpt. I give it specific small problems to resolve and some context. At most 100 lines of code. If it gets more the quality goes to shit. In fact copilot feels like chatgpt that was given too much context.
I hear it all the time on HN that people are producing entire apps with LLMs, but I just don't believe it. All of my experiences with LLMs have been that for anything that isn't a braindead-simple for loop is just unworkable garbage that takes more effort to fix than if you just wrote it from scratch to begin with. And then you're immediately met with "You're using it wrong!", "You're using the wrong model!", "You're…
It works well as a smart documentation search where you can ask follow-up questions or when you know what the output should look like if you see it but can't type it directly from the memory.
For code assistants (aka copilot / cursor), it works if you don't care about the code at all and ok with any solution if it's barely working (I'm ok with such code for my emacs configuration).
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Waymo was providing 10,000 weekly autonomous rides in August 2023, 50,000 in June 2024, and 100,000 in August 2024. Not everything has this trajectory, and it took 10 years more than expected. But it's coming. Not saying AI will be the same, but underestimating the impact of having certain outputs 100x cheaper, even if many times crappier seems like a losing bet, considering how the world has gone so far.
Waymo is a great example, actually. They serve Phoenix, SF and LA. Those locations aren’t chosen at random, they present a small subset of all the weather and road conditions that humans can handle easily. So yes: handling 100,000 passengers is a milestone. The growth from 10,000 to 100,000 implies it’s going to keep growing exponentially. But eventually they’re going to encounter stuff like Midwest winters that can…
"People in San Francisco tag a driverless car"
https://www.reddit.com/r/CrazyFuckingVideos/comments/1fqcpq2...
About driverless cars, new tech adoptions often start slow, until the iceberg tips and then it's very quick change. Like mobile phones today.
I remember thinking before smartphones that had entire-day battery and good touchscreens: These people really think population will use phones more than desktop computers? Here we are.
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There's no accounting for taste, but keep in mind that all of these services are currently losing money, so how much would you actually be willing to pay for the service you're currently getting in order to let it break even? There was a report that Microsoft is losing $20 for every $10 spent on Copilot subscriptions, with heavy users costing them as much as $80 per month. Assuming you're one of those heavy users, wo…
I would pay hundreds of dollars per month for the combination of cursor and claude - I could not get my head around it when my beginner lever colleague said "I just coded this whole thing using cursor". It was an entire web app, with search filters, tree based drag and drop GUIs, the backend api server, database migrations, auth and everything else. Not once did he need to ask me a question. When I asked him "how lon…
In niche situations it's not helpful at all in writing code that works (or even close). It is helpful as a quick lookup for docs for libs or functions you don't use much, or for gotchas that you might otherwise search StackOverflow for answers to.
It's good for quick-and-dirty code that I need for one-off scripts, testing, and stuff like that which won't make it into production.
Remember when the Russians mocked Elon Musk for his rocket purchase? Remember when Blockbuster mocked Netflix? Remember when Steve Ballmer mocked the iPhone? Remember when Zuck was mocked for the Metaverse? I'm pretty sure Sam would have heard similar mockings like "You can't take on Google behemoth, podcaster bro" before. But the sad part is, the newer tech generation is so brainwashed that it takes the side of the…
One of these things is not like the others. You listed 3 things proven silly with hindsight, then you post one comment that is mere hopeful wishing.
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"Plausible-looking but incorrect sentences" is cheap, reflexive cynicism. LLMs are an incredible breakthrough by any reasonable standard. The reason to be optimistic about further progress is that we've seen a massive improvement in capabilities over the past few years and that seems highly likely to continue for the next few (at least). It's not going to scale forever, but it seems pretty clear that when the dust se…
>when the dust settles we'll have LLMs significantly more powerful than the current cutting edge -- which is already useful. LLMs, irrespective of how powerful, are all subject to the fundamental limitation that they don't know anything. The stochastic parrot analogy remains applicable and will never be solved because of the underlying principles inherent to LLMs. LLMs are not the pathway to AGI.
That effective theory is knowledge, literally.
People harping about “stochastic parrot” are just people repeating a shallow meme — ironically, like a stochastic parrot.
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And I would go further… these “common tasks” cover 80% of the work in even the most demanding engineering or research positions.
That’s absolutely not my experience. I struggle to find tasks in my day to day work where LLMs are saving me time. One reason is that the systems and domains I work with are hardly represented at all on the internet.
But it's also utterly failed to handle mundane tasks, like porting legacy code from one language and ecosystem to another, which is frankly surprising to me because I'd have assumed it would be perfectly suited for that task.
Based. The more I read about TSMC, the more I like them.
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I can't understand how anyone can use these tools (copilot especially) to make entire projects from scratch and expand them later. They just lead you down the wrong path 90% of the time. Personally I much prefer Chatgpt. I give it specific small problems to resolve and some context. At most 100 lines of code. If it gets more the quality goes to shit. In fact copilot feels like chatgpt that was given too much context.
I hear it all the time on HN that people are producing entire apps with LLMs, but I just don't believe it. All of my experiences with LLMs have been that for anything that isn't a braindead-simple for loop is just unworkable garbage that takes more effort to fix than if you just wrote it from scratch to begin with. And then you're immediately met with "You're using it wrong!", "You're using the wrong model!", "You're…
When they say treat it like an intern, I'm so confused. An intern is there to grow and hopefully replace you as you get promoted or leave. The tasks you assign to him are purposely kept simple for him to learn the craft. The monotonous ones should be done by the computer.
[0]: https://gist.github.com/simonw/97e29b86540fcc627da4984daf5b7...
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> 20 year software engineering career is about to change I have also been developing for 20+ years. And have heard the exact same thing about IDEs, Search Engines, Stack Overflow, Github etc. But in my experience at least how fast I code has never been the limiting factor in my project's success. So LLMs are nice and all but isn't going to change the industry all that much.
There will be a whole industry of people who fix what AI has created. I don't know if it will be faster to build the wrong thing and pay to have it fixed or to build the right thing from the get go, but after having seen some shit, like you, I have a little idea.
If nothing really changes in 3-5 years, then I'd call it a flop. But the writing is on the wall that "scale = smarts", and what we have today still looks like a foundational stage for LLM's.