I'm not sure I want another layer of indirection personally, and I'm guessing an updated Claude model will reign this in at some point. I have however created a skill I call "deslop" and I invoke it to clean up Claude output when it goes off the rails. Here's the skill if anybody is curious:
*Meta commentary.* Sentences about the document, the diagram, the reader, or the
writing itself ("the split across this diagram is the whole point", "a reader who
assumes X will be wrong", "as we'll see below"). Delete the frame and keep the fact
it was wrapped around. If there is no fact underneath, delete the sentence.
LLM doesn't need soft skills, but just knowing how to write a prompt to get the correct percentage values in a RAG and get the result close to your expectations out. Well it might be different based on the training data, which ai company, and how much you're paying.
People are different, they will be sometime affected by their emotional situation, surrounding, no matter how much they're being paid, You need to understand their mental situation, did he got scolded by the upper management today? He might not be on his best of his capacity right now. Or they might've pulled an all nighter and really not in their best position.
If someone going to treat people like an LLM, definitely is not going to have good time
Ironically, the same applies to AI. We seem to get demonstrably better results when speaking to models encouragingly [1]. It's a lot easier to re-establish goodwill when you can clear the memory and context of a program vs. a human though.
We don't know if it's all marketing stunts, or real, do we now?
> It's a lot easier to re-establish goodwill when you can clear the memory and context of a program vs. a human though.
That's the point. You can't erase human memories, experiences, (unless you bonk their head hard enough according to movies in my subcontinent, another bonk might reverse too) which will definitely affect their decisions and results.
But if you are managing AI agents you dont' need "soft skills" do you? You don't need to be especially nice to the AI, or symphatize with it, or have fun with it to build trust,
I would imagine that managing a team of AI-agents is totally different from managing a team of people.
> But if you are managing AI agents you dont' need "soft skills" do you?
“Soft skills” in management just means figuring out how to get what you want from the people you have available to you. In that respect those skills translate to using an LLM.
Figuring out how to get what you want from people would seem to me to be a very different skill than figuring out how to get what you want from AI agents.
For instance, how do you motivate people to work long hours, put in extra effort, feel proud of their work? How would you do that with AI?
They may be softer, but they're really not an identical set of soft-skills.
To illustrate the difference, imagine: "Hey, you've got all those soft-skills from tweaking the AI stuff, right? I need you to motivate Bob to get his head back in the game, but without causing him to resign."
Talking to an LLM is not a skill, just like using Google is not a skill.
Why? One, the companies like Google or Anthropic or OpenAI are working hard for it not to be a skill. That's the whole point. Second, these system are opaque, so there is no understanding to happen, only superstition, which might be wrong or change tomorrow.
I beg to differ. It is a simple skill that a great many have, but that doesn't make it “not a skill” - there are certainly many that don't have it, or don't want to practise it. Though I wouldn't name it specifically for Google, it is the more general “finding information online” skill which feels more specific because for many people these days it doesn't extend much beyond using Google or whatever their browser's default search service is.
People without the skill are quite evident: many of the closed duplicates on SO and similar sites are due to people lacking the skill to find information in old answers and effectively just asking others to look things up for them, the same for this week's 20th+ “my first layer has these bumps and gaps, what is going on?” question on any 3D printing forum (facebook groups etc.) that could be answered by scrolling down a few posts, and I'm sure the equivalent happens in groups serving any other plaything/hobby/skill/whatever.
Neither are skills that a large portion of users of those services pursue to any meaningful extent, I'll grant you that. They also certainly are not synonymous with the term "soft skills" as I know it. So I think I am on your side of the fence on that part any way.
I feel like if they were skills under a reasonable definition, you should be able to name an expert in these skills, and how do we know they are an expert.
But I think you can't. It seems to me, instead, one is better at googling/prompting the better they are in a particular domain, but it only applies in that domain. Like knowing a jargon is not a skill, knowing the domain is.
> you should be able to name an expert in these skills
There are certainly local “finding information online” experts in many families and social groups.
> and how do we know they are an expert
They are the ones who get mentioned a lot in conversations in the manner “I'll have to ask [name]” with the implication that [name] will look up the information or know it from previous occasions people (possibly this specific person and [name] is getting sick of them asking and not remembering simple answers!) have asked.
Soft skills include: handling change under ambiguity, critical thinking under pressure, self-awareness, prioritizing, motivating and guiding others without relying on authority, navigating disagreement constructively.
And of course if you point any of this out people go “Well many people are good at it, people getting bad results are just bad at it.” The constant refrain of “you’re just using it wrong” has become very tiresome.
If you can always say “just use it right” every time a critique comes up, then we aren’t having an honest conversation about the limitations of these tools.
It is impossible to duplicate results with an LLM. This strikes me as a serious barrier to calling it a proper skill. If you can’t even somewhat replicate the results you can’t really improve the input consistently. You can’t become “skilled” at it if you can’t even reproduce what you did.
If you enter the same prompt 3 times the results are of pretty significantly different quality. ChatGPT literally has you A/B test for them sometimes. They’re right to call it superstition - it feels like we’re making incantations and hoping for the best a lot of the time.
Prompting LLM’s still feels like a constant game of guess and check. At best you can argue it’s an educated guess. I don’t know about you but I didn’t learn math by guessing and checking, I frequently had to work backwards and review where I went wrong and/or I had the answer given to me with the work shown so I can learn. I can’t do that with a prompt. When I get bad results (which we all frequently do) I just guess what it didn’t like, try again, and pray for a better result.
Me typing into emacs is also not replicable, until I git push. Me with emacs and a good coding LLM can generate systems that I see as good, in my extensive professional judgement. For coding, for production grade code, the LLM will mostly be a tool in the hands of the professional. There is a new category of disposable code, which I think will be useful for many adhoc investigations; in neither case is replicability a serious requirement.
I do a lot of guessing and validating in learning maths. It is pretty efficient way to build that conceptual understanding. I even try to predict the next big theorem as I am listening to a lecture or reading a text book. It's engaging.
And honestly, I am enjoying learning this new way to make code I am pleased with. Using the LLM effectively and for quality deliverables is different from typing in many surface ways, but modularity of thought, iterative design and implementation, simplicity and generality, documentation, all still pay off.
These conversations always veer into “well I find it useful so I disagree.” I’m not saying anything about utility, I just think that system prompting is still very much hopes and prayers. We’re having a discussion about whether or not it’s a hard skill one can learn, I think that’s incredibly debatable
> It is impossible to duplicate results with an LLM
But that's the fundamental property of it - it is stochastic by nature. The skill is to learn how to sandwich deterministic logic between layers of randomness. Determinism doesn't live in the model. It lives in the harness you build around it. You can't make the model deterministic (it simply cannot be), so you make the system deterministic instead. Validation before, validation after, the randomness stays contained in the middle.
Talking to an LLM is not a skill, having a meaningful conversation leading to practical outcomes is.
If you ask me to write an email, all 3 variations will be more or less the same, except it will almost always improve with each iteration. 3 cracks at a prompt is 3 separate, unrelated attempts. None of them informs the other. In fact, you run the risk of making it worse if you include previous versions.
> 3 cracks at a prompt is 3 separate, unrelated attempts. None of them informs the other.
Thats where the "skill" part comes in. Like your 3 attempts at email that `almost always improve`. This is where you input the "previous versions", not directly back into the model. It's the "soft skill" of being flexible and adjusting based on how an entity responds to the input. Not learning how to adjust the input (by using previous attempts to inform the next) to more optimally direct the output given the state of the llm (chat? agent? model, effort lvl etc) it will seem chaotic.
The skills wont make an llm* deterministic, same as applying these soft skills to people. Give the same input to a person in `3 times separate, unrelated attempts` you are likely to get 3 different seemly chaotic outcomes. The "skill" is in being able to take what you received as output the first time, and make adjustments based on previous attempts while accounting for the state of the entity for the next attempt.
* In no way am I saying they are conscious beings or whatever nonsense by using people in the analogy. There are, however, parallels in how a set of soft skills (and this is why they are "soft" skills) can be used to get more optimal results from an entity that should never be expected to act as a pure function.
I don't think soft skills describes it in the traditional sense. The skillset largely needed with LLMs is more akin to being an editor or qa tester.
I suppose you could describe having the modesty to admit to yourself when you don't understand and research something deeper could be described as a soft skill, but I'd say it's a stretch. You are dealing with yourself in that scenario, not others.
He was showing how complex and expensive a data center is and giving them real-world visceral experience that inefficient code has real-world implications.
Do you have SLO/SLAs defined? Are you monitoring performance? CPU usage? Cost increases?
You should have plenty of data to review regularly and push back on any teams that are causing problems. That's a process problem and you need a process for it.
Trace the increase back to specific deployments, call out those teams, and make them fix their shit.
Yes to all, but it's just me responsible for monitoring performance/cost. I either push back or fix it myself, but I am behind. So this is an attempt to give/push more ownership to the teams.
The parent didn't go into any detail. I can. Homebrew has a history of ripping out your foundation underneath you. One day you are on Python 3.8, then next day you are on Python 3.10 and all your packages are broken. MacPorts doesn't do that.
Now, whether you should you be using the Homebrew Python is a completely different question. YMMV for other platforms managed via Homebrew.
I've traditionally used MacPorts for dev tooling and Homebrew for everything else, but with more aggressive adoption of tooling like uv an nvm I'm not sure the different really matters for me anymore.
Exactly. Same with PHP, MySQL etc…
Also they just block old versions and dont let you install them, you have to jump through a lot of hoops to use an old PHP version for example, so in no way developer friendly.
In the end I realized that Brew is a package manager for consumers, and as a professional i should’nt keep fighting it.
A lot of that is simply formula authors / application devs who don't know what they're doing (python@3.10 and other versions are a thing, and have been for quite a while now, but they're not always used and devs don't always keep track of the version they need) and people not updating their software for years (pythons are on a 5 year cycle everywhere, homebrew included: https://devguide.python.org/versions/ and https://formulae.brew.sh/formula/python@3.10 ).
Python in particular is well known to not be a stable target. For anyone. By design. If you expect long term use of a specific version of code, use a different language. It is not at all homebrew's fault that they're how many people discover that.
pyenv has been standard tooling for far longer than uv. depending on package manager supplied Python packages only makes sense if you’re running rhel or Debian or something and your application is packaged/deployed/the maintenance path uses dnf/apt. Otherwise you should always use a venv and use an out of package manager update mechanism. Like, in a broader sense, vendoring dependencies only makes sense if you’re shipping an application, not on a dev box.
That's what the GP means as well, you can use pyenv and uv to install multiple versions of Python and create envs with whichever version you want to use.
I switched to mise too for all my dev tooling. It just works so nicely for all kinds of ecosystems. I can use the same tool for Python, Node, Java, whatever and it just works.
I want to see a graph or flame graph or something akin to how Temporal renders workflows in real-time. I want to follow along as all the different sub-agents and tool calls do their work. I also want to know how much this is costing me at each step.
Just like with a Temporal workflow, I don't care how things work 99% of the time. But that last 1% when I really need to dig in and understand how and where things went sideways this type of view would be invaluable.
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