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> It handles complex coding tasks with minimal prompting... I find it interesting how marketers are trying to make minimal prompting a good thing, a direction

> It handles complex coding tasks with minimal prompting... I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize. Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc. Pushing the models to just do what they think its best is a weird direction. There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head. Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute.

ls-a 1y ago
This works well with managers. They think if the task title on jira is a one liner, then it's that simple to implement.
- consp 1y ago
  Usually it's exactly the opposite, more often due to missing containment and requirements so you get a vague oneliner. Infinity hours it is ...
  - ls-a 1y ago
    Then you have engineers that will agree with the manager on everything
c048 1y ago
This is why I don't listen at all to the fearmongers that say programmers will disappear. At most, our jobs will slightly change. There will always be people that describe a problem, and you'll always need people actually figuring out what's actually wrong.
- ACCount36 1y ago
  What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? Let alone "this wouldn't EVER be capable of that"?
  - benterix 1y ago
    > What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? I wouldn't say they're completely incapable. * They can spot (and fix) low hanging fruit instantly * They will also "fix" things that were left out there for a reason and break things completely * even if the code base fits entirely in their context window, as does the complete company knowledge base, including Slack conversations etc., the proposed solutions sometimes take a very strange turn, in spite of being correct 57.8% of the time.
    - ACCount36 1y ago
      That's about right. And this kind of performance wouldn't be concerning - if only AI performance didn't go up over time. Today's AI systems are the worst they'll ever be. If AI is already capable of doing something, you should expect it to become more capable of it in the future.
      - binary132 1y ago
        why is “the worst they’ll ever be” such a popular meme with the AI inevitabilist crowd and how do we make their brains able to work again?
        ACCount36 1y ago
        It's popular because it's true. By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable.
        disgruntledphd2 1y ago
        > By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable. OK, so where is the new data going to come from? Fundamentally, LLMs work by doing token prediction when some token(s) are masked. This process (which doesn't require supervision hence why it scaled) seems to be fundamental to LLM improvement. And basically all of the AI companies have slurped up all of the text (and presumably all of the videos) on the internet. Where does the next order of magnitude increase in data come from? More fundamentally, lots of the hype is about research/novel stuff which seems to me to be very, very difficult to get from a model that's trained to produce plausible text. Like, how does one expect to see improvements in biology (for example) based on text input and output. Remember, these models don't appear to reason much like humans, they seem to do well where the training data is sufficient (interpolation) and do badly where there isn't enough data (extrapolation). I'd love to understand how this is all supposed to change, but haven't really seen much useful evidence (i.e. papers and experiments) on this, just AI CEOs talking their book. Happy to be corrected if I'm wrong.
- croes 1y ago
  The problem isn’t the AI but the management that believes the PR. It doesn’t matter if AI can replace developers but if the management thinks it can.
  - breakpointalpha 1y ago
    That's only a problem in the short term. Watch the company fire 50% of the engineering team then hit a brick wall at 100mph.
nojito 1y ago
Because people are overprompting and creating crazy elaborate harnesses. My prompts are maybe 1 - 2 sentences. There is a definite skill gap between folks who are using these tools effectively and those who do not.
KronisLV 1y ago
> Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute. RooCode supports various modes https://docs.roocode.com/basic-usage/using-modes https://docs.roocode.com/basic-usage/using-modes For example, you can first use the Ask mode to explore the codebase and answer your questions, as well as ask you its own about what you want to do. Then, you can switch over to the Code mode to do the actual implementation, or the model itself will ask you to switch to it in other modes, because it's not allowed to change files in the Ask mode. I think that approach works pretty well, especially when you document what needs to be done in a separate Markdown file or something along the lines of it, that can be then referenced if you have to clean the context, like a new refactoring task for what's been implemented. > I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize. This seems like a good thing, though. You're still allowed to be as specific as you want to, but the baseline is a bit better.
igleria 1y ago
> I find it interesting how marketers are trying to make minimal prompting a good thing They do that because IMHO the average person seems to prefer something to be easy, rather than correct.
Ancapistani 1y ago
> Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc. Sure - but you're being specific about the acceptance criteria, not the technical implementation details, right? That's where the models I've been using are at the moment in terms of capability; they're like junior engineers. They know how to write good quality code. If I tell them exactly what to write, they can one-shot most tasks. Otherwise, there's a good chance the output will be spaghetti. > There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head. My primary agentic code generation tool at the moment is OpenHands (app.all-hands.dev). Every time it makes an architectural decision I disagree with, I add a "microagent" (long-term context, analogous to CLAUDE.md or Devin's "Knowledge Base"). If that new microagent works as expected, I incorporate it into either my global or organization-level configs. The result is that it gets more and more aligned with the way I prefer to do things over time.

ls-a 1y ago

This works well with managers. They think if the task title on jira is a one liner, then it's that simple to implement.

consp 1y ago
Usually it's exactly the opposite, more often due to missing containment and requirements so you get a vague oneliner. Infinity hours it is ...
- ls-a 1y ago
  Then you have engineers that will agree with the manager on everything

c048 1y ago

This is why I don't listen at all to the fearmongers that say programmers will disappear. At most, our jobs will slightly change. There will always be people that describe a problem, and you'll always need people actually figuring out what's actually wrong.

ACCount36 1y ago
What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? Let alone "this wouldn't EVER be capable of that"?
- benterix 1y ago
  > What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? I wouldn't say they're completely incapable. * They can spot (and fix) low hanging fruit instantly * They will also "fix" things that were left out there for a reason and break things completely * even if the code base fits entirely in their context window, as does the complete company knowledge base, including Slack conversations etc., the proposed solutions sometimes take a very strange turn, in spite of being correct 57.8% of the time.
  - ACCount36 1y ago
    That's about right. And this kind of performance wouldn't be concerning - if only AI performance didn't go up over time. Today's AI systems are the worst they'll ever be. If AI is already capable of doing something, you should expect it to become more capable of it in the future.
    - binary132 1y ago
      why is “the worst they’ll ever be” such a popular meme with the AI inevitabilist crowd and how do we make their brains able to work again?
      - ACCount36 1y ago
        It's popular because it's true. By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable.
        disgruntledphd2 1y ago
        > By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable. OK, so where is the new data going to come from? Fundamentally, LLMs work by doing token prediction when some token(s) are masked. This process (which doesn't require supervision hence why it scaled) seems to be fundamental to LLM improvement. And basically all of the AI companies have slurped up all of the text (and presumably all of the videos) on the internet. Where does the next order of magnitude increase in data come from? More fundamentally, lots of the hype is about research/novel stuff which seems to me to be very, very difficult to get from a model that's trained to produce plausible text. Like, how does one expect to see improvements in biology (for example) based on text input and output. Remember, these models don't appear to reason much like humans, they seem to do well where the training data is sufficient (interpolation) and do badly where there isn't enough data (extrapolation). I'd love to understand how this is all supposed to change, but haven't really seen much useful evidence (i.e. papers and experiments) on this, just AI CEOs talking their book. Happy to be corrected if I'm wrong.
croes 1y ago
The problem isn’t the AI but the management that believes the PR. It doesn’t matter if AI can replace developers but if the management thinks it can.
- breakpointalpha 1y ago
  That's only a problem in the short term. Watch the company fire 50% of the engineering team then hit a brick wall at 100mph.

nojito 1y ago

Because people are overprompting and creating crazy elaborate harnesses. My prompts are maybe 1 - 2 sentences. There is a definite skill gap between folks who are using these tools effectively and those who do not.

KronisLV 1y ago

> Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute. RooCode supports various modes https://docs.roocode.com/basic-usage/using-modes https://docs.roocode.com/basic-usage/using-modes For example, you can first use the Ask mode to explore the codebase and answer your questions, as well as ask you its own about what you want to do. Then, you can switch over to the Code mode to do the actual implementation, or the model itself will ask you to switch to it in other modes, because it's not allowed to change files in the Ask mode. I think that approach works pretty well, especially when you document what needs to be done in a separate Markdown file or something along the lines of it, that can be then referenced if you have to clean the context, like a new refactoring task for what's been implemented. > I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize. This seems like a good thing, though. You're still allowed to be as specific as you want to, but the baseline is a bit better.

igleria 1y ago

> I find it interesting how marketers are trying to make minimal prompting a good thing They do that because IMHO the average person seems to prefer something to be easy, rather than correct.

Ancapistani 1y ago

> Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc. Sure - but you're being specific about the acceptance criteria, not the technical implementation details, right? That's where the models I've been using are at the moment in terms of capability; they're like junior engineers. They know how to write good quality code. If I tell them exactly what to write, they can one-shot most tasks. Otherwise, there's a good chance the output will be spaghetti. > There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head. My primary agentic code generation tool at the moment is OpenHands (app.all-hands.dev). Every time it makes an architectural decision I disagree with, I add a "microagent" (long-term context, analogous to CLAUDE.md or Devin's "Knowledge Base"). If that new microagent works as expected, I incorporate it into either my global or organization-level configs. The result is that it gets more and more aligned with the way I prefer to do things over time.