261–270 of 342 posts
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It’s doable as it’s what I use to experiment. Ollama + CodeGPT IntelliJ plugin. It allows you to point at a local instance.
I also use Ollama for coding. I have a 32G M2 Mac, and the models I can run are very useful for coding and debugging, as well as data munging, etc. That said, sometimes I also use Claude Sonnet 3.5 and o1. (BTW, I just published an Ollama book yesterday, so I am a little biassed towards local models.)
I’m a hobby programmer who never worked a programming job. Last week I was bored, I asked o1 to help me to write a Solitaire card game using React because I’m very rusty with web development. The first few steps were great. Guided me to install things and setup a project structure. The model even generated codes for a few files. Then something went wrong, the model kept telling me what to do in vague, but didn’t outp…
I'm not too optimistic about the future of software development if juniors are turning to AI to do those early projects for them.
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> That he says LLMs make him more productive at all as a hands-on developer, especially around first drafts on a new idea, means a lot to me personally. There is likely to be a great rift in how very talented people look at sharper tools. I've seen the same division pop up with CNC machines, 3d printers, IDEs and now LLMs. If you are good at doing something, you might find the new tool's output to be sub-par over wha…
> I got into this profession simply because I could Ctrl-Z to the previous step much more easily than my then favourite chemical engineering goals. That is interesting. Asking as a complete ignoramus - is there not a way to do this now? Like start off with a 100 of reagent and at every step use a bit and discard if wrong
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I have also many years of programming experience and find myself strongly "accelerated" by LLMs when writing code. But, if you think at it, it makes sense that many seasoned programmers are using LLMs better. LLMs are a helpful tool, but also a hard-to-use tool, and in general it's fair to think that better programmers can do a better use of some assistant (human or otherwise): better understanding its strengths, ide…
That's really interesting. What are the most important things you've learned to do with the LLMs to get better results? What do your problem descriptions look like? Are you going back and forth many times, or crafting an especially-high-quality initial prompt?
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That's really interesting. What are the most important things you've learned to do with the LLMs to get better results? What do your problem descriptions look like? Are you going back and forth many times, or crafting an especially-high-quality initial prompt?
I'm posting a set of videos on my YT channel where I'll show the process I follow. Thanks!
> A lot of the value I personally get out of chat-driven programming is I reach a point in the day when I know what needs to be written, I can describe it, but I don’t have the energy to create a new file, start typing, then start looking up the libraries I need... LLMs perform that service for me in programming. They give me a first draft, with some good ideas, with several of the dependencies I need, and often some…
I think your biggest takeaway should be that they person writing the blog post is extremely well-known versed in programming and has labored over code for hours, along with writing tests, debugging, etc. He knows what he would like because it's second nature. He was able to get the best from the LLM because his vision of what the code should look like helped craft a solid prompt. Newer people into programming might n…
One interesting bit of context is that the author of this post is a legit world-class software engineer already (though probably too modest to admit it). Former staff engineer at Google and co-founder / CTO of Tailscale. He doesn't need LLMs. That he says LLMs make him more productive at all as a hands-on developer, especially around first drafts on a new idea, means a lot to me personally. His post reminds me of an…
> That he says LLMs make him more productive at all as a hands-on developer, especially around first drafts on a new idea, means a lot to me personally. There is likely to be a great rift in how very talented people look at sharper tools. I've seen the same division pop up with CNC machines, 3d printers, IDEs and now LLMs. If you are good at doing something, you might find the new tool's output to be sub-par over wha…
IMO, LLMs are super fast predictive input and hallucinatory unzip; files to be decompressed don't have to exist yet, but input has to be extremely deliberate and precise.
You have to have a valid formula that gives the resultant array that don't require no more than 100 IQ to comprehend, and then they unroll it for you into the whole code.
They don't reward trial and error that much. They don't seem to help outsiders like 3D printers did, either. It is indeed a discriminatory tool as in it mistreats amateurs.
And, by the way, it's also increasingly obvious to me that assuming pro-AI posture more than what you would from purely rational and utilitarian standpoint triggers a unique mode of insanity in humans. People seem to contract a lot of negativity doing it. Don't do that.
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> That he says LLMs make him more productive at all as a hands-on developer, especially around first drafts on a new idea, means a lot to me personally. There is likely to be a great rift in how very talented people look at sharper tools. I've seen the same division pop up with CNC machines, 3d printers, IDEs and now LLMs. If you are good at doing something, you might find the new tool's output to be sub-par over wha…
Intersting. So engineers that like to iterate and explore are more likely to like LLMs. Whereas engineers that like have a more rigid specific process are more likely to dislike LLMs.
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I believe it’s more that people hate trying new tools because they’ve already made their choice and made it their identity. However, there are also people who love everything new and jump onto the latest hype too. They try new things but then immediately advocate it without merit. Where are the sane people in the middle?
As an experienced software developer, I paid for ChatGPT for a couple of months, I trialed Gemini Pro for a couple of months, and I've used the current version of Claude. I'd be happy if LLMs could produce working code as often and as quickly as the evangelist claim, but whenever I try to use LLM to work on my day to day tasks, I almost always walk away frustrated and disappointed - and most of my work is boring on t…
* Information lookup
-- when search engines are enshittified and bogged down by SEO spam and when it's difficult to transform a natural language request into a genuinely unique set of search keywords
-- Search-enabled LLMs have the most up to date reach in these circumstances but even static LLMs can work in a pinch when you're searching for info that's probably well represented in their training set before their knowledge cutoff
* Creatively exploring a vaguely defined problem space
-- Especially when one's own head feels like it's too full of lead to think of anything novel
-- Watch out to make sure the wording of your request doesn't bend the LLM too far into a stale direction. For example naming an example can make them tunnel vision onto that example vs considering alternatives to it.
* Pretending to be Stack Exchange
-- EG, the types of questions one might pose on SE one can pose to an LLM and get instant answers, with less criticism for having asked the question in the first place (though Claude is apparently not above gently checking in if one is encountering an X Y problem) and often the LLM's hallucination rate is no worse than that of other SE users
* Shortcut into documentation for tools with either thin or difficult to navigate docs
-- While one must always fact-check the LLM, doing so is usually quicker in this instance than fishing online for which facts to even check
-- This is most effective for tools where tons of people do seem to already know how the tool works (vs tools nobody has ever heard of) but it's just not clear how they learned that.
* Working examples to ice-break a start of project
* Simple automation scripts with few moving parts, especially when one is particular about the goal and the constraints
-- Online one might find example scripts that almost meet your needs but always fail to meet them in some fashion that's irritating to figure out how to coral back into your problem domain
-- LLMs have deep experience with tools and with short snippets of coherent code, so their success rate on utility scripts are much higher than on "portions of complex larger projects".