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> Learning how to use LLMs in a coding workflow is trivial. There is no learning curve. You can safely ignore them if they don’t fit your workflows at the moment.
How much of your workflow or intuition from 6 months ago is still relevant today? How long would it take to learn the relevant bits today?
Keep in mind that Claude Code was released less than 6 months ago.
I have a biased opinion since I work for a background agent startup currently - but there are more (and better!) out there than Jules and Copilot that might address some of the author's issues.
And those mythical better tools tools that you didn't even bother to mention are?
LLM’s are basically glorified slot machines. Some people try very hard to come up with techniques or theories about when the slot machine is hot, it’s only an illusion, let me tell you, it’s random and arbitrary, maybe today is your lucky day maybe not. Same with AI, learning the “skill” is as difficult as learning how to google or how to check stackoverflow, trivial. All the rest is luck and how many coins do you ha…
We know what random* looks like: a coin toss, the roll of a die. Token generation is neither.
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This is all just ignorance. We've all worked with LLMs and know that creating an effective workflow is not trivial and it varies based on the tool.
Define "not trivial". Obviously, experience helps, as with any tool. But it's hardly rocket science. It seems to me the biggest barrier is that the person driving the tool needs to be experienced enough to recognize and assist when it runs into issues. But that's little different from any sophisticated tool. It seems to me a lot of the criticism comes from placing completely unrealistic expectations on an LLM. "It's…
I don’t have to debug Emacs every day to write code. My CI workflow just runs every time a PR is created. When I type ‘make tests’, I get a report back. None of those things are perfect, but they are reliable.
LLM’s are basically glorified slot machines. Some people try very hard to come up with techniques or theories about when the slot machine is hot, it’s only an illusion, let me tell you, it’s random and arbitrary, maybe today is your lucky day maybe not. Same with AI, learning the “skill” is as difficult as learning how to google or how to check stackoverflow, trivial. All the rest is luck and how many coins do you ha…
It’s mostly on point though. Although, in recent years I’ve been assigned to manage and plan projects at work, and the skills I’ve learnt from that greatly help to get effective results from an LLM I think.
LLM’s are basically glorified slot machines. Some people try very hard to come up with techniques or theories about when the slot machine is hot, it’s only an illusion, let me tell you, it’s random and arbitrary, maybe today is your lucky day maybe not. Same with AI, learning the “skill” is as difficult as learning how to google or how to check stackoverflow, trivial. All the rest is luck and how many coins do you ha…
This is not a good analogy. The parameters of slot machines can be changed to make the casino lose money. Just because something is random, doesn't mean it is useless. If you get 7 good outputs out of 10 from an LLM, you can still use it for your benefit. The frequency of good outputs and how much babysitting it requires determine whether it is worth using or not. Humans make mistakes too, although way less often.
Earlier quoted context omitted.
And those mythical better tools tools that you didn't even bother to mention are?
Presumably if they did, they would be accused of promoting their startup :)
And promoting own startups are usually okay if that is phrased okay :)
Do they? I’ve found Clojure-MCP[1] to be very useful. OTOH, I’m not attempting to replace myself, only augment myself.
Earlier quoted context omitted.
Presumably if they did, they would be accused of promoting their startup :)
But he said there are more and better out there. "More" implies more than one :) And promoting own startups are usually okay if that is phrased okay :)
Devin is perhaps the one that is most fully featured and I believe has been around the longest. Other examples that seem to be getting some attention recently are Warp, Cursor's own background agent implementation, Charlie Labs, Codegen, Tembo, and OpenAI's Codex.
I do not work for any of the aforementioned companies.