大语言模型让我变得不再敏锐了。
LLMs are making me lose my savviness

原始链接: https://pgaleone.eu/ai/2026/08/29/losing-savviness/

作为一名资深的软件工程师和“创客”,作者表达了因 AI 编程助手的普及而产生的深切失落感与倦怠感。尽管这些工具能够实现快速原型设计与执行,但作者认为它们剥夺了工程学中至关重要的“技艺”核心。 对于作者而言,开发的乐趣始终存在于探索的过程之中:深度学习、反复试错,以及通过亲手解决复杂问题所获得的宝贵“经验”。通过自动化实现过程,大语言模型(LLM)消除了认知挑战,将创造性工作变成了一种提示与输出验证的重复循环。作者担心,对这些工具的依赖不仅让他们失去了创造的满足感,还面临着自身专业技能退化的风险。尽管企业界施加了追求速度的压力,但作者对 AI 生成代码的长期可维护性和价值仍持怀疑态度。归根结底,这篇文章忧郁地反思了对效率的追求是如何在无意中牺牲了定义工程职业的人文乐趣与智力成长。

Hacker News 上的一场讨论探讨了在创意或技术工作中使用大语言模型(LLM)的利弊,起因是人们担心人工智能工具会削弱个人的“精明程度”。 这场争论主要围绕两种观点展开: 1. **产出与过程:** 一些用户认为,LLM 等工具只是能够拓宽个人能力的辅助手段,重点在于最终产出。另一些人则认为,手工作业——即亲身学习和解决问题的过程——才是真正技术和成就感的来源。 2. **类比的有效性:** 评论者们对使用传统工艺类比(例如将 AI 比作电动工具)展开了辩论。批评者指出,与物理工具不同,LLM 并不可靠(容易出现“幻觉”),且可能会掩盖任务背后的核心机制,从而可能导致深度技术直觉的丧失。 最终,该讨论串揭示了一个根本性的分歧:一方是看重最终成果效率的创作者,另一方是优先考虑探索过程中的智力严谨性的创作者。共识认为,这两种路径都是合理的,但它们对应着对技艺的不同动机与期望。
相关文章

原文

I don’t know what’s going on. I always wanted to solve problems and create things, but now that I have access to a tool that’s able to speed up the whole design and development process… I’m just bored.

I don’t feel the passion of building anymore, mainly because I’m not building at all. There’s no craft in letting something else build on your behalf, there’s no real thought or design in writing prompts. There’s no engineering or challenge left. It’s just: prompt, evaluate the output, adjust, and repeat. What the hell is this? Sometimes I use them to actually learn something - but it’s a different way of learning. It’s like reading a summary or a description of some math formula, without applying it - quite pointless.

I have always been a “maker” - in my own way. I used to study topics in depth, spending hours and sleepless nights thinking about problems and solutions. I’ve experienced that great feeling of “this is a great idea” coming out of nowhere while doing unrelated activities, and having to rush back at my desk to write it down and implement it. That was great - it was rewarding, it was really meaningful. My brain was working and I felt satisfied every time I looked at what I created. Perhaps this is the result of 5 years of university studying computer engineering, but I guess not, it’s something I’ve always done and the engineering mindset just fit.

Over the years, I’ve built software of all kinds: from ML libraries to web development tools, passing through websites, wrote an infinite number of scripts for Linux system administration, set up servers, created online communities of tech enthusiast, designed database architectures. And a lot more - my guilty pleasure of mixing languages and technologies that I didn’t know, just for the fun of experiment, learning, challenge myself, and spend some time having fun.

Now, all of that feels… gone? I can just have an idea, ask the model-of-the-month to implement it, and - wow - it’s there. Written in no time and with code quality that improves month after month. If I add guardrails, rules, skills, and design documents upfront, the model generates pretty much the same thing I would have written (not really - but you got the idea). Sure, it still depends on the size and complexity of the idea, but anyway, the possibility of prototyping in no time is nice — but terrible?

To be clear, no one is forcing me to do this on my free time. On the job - and apparently across corporate tech in general - there’s this huge pressure for using those tools to not “get left behind”. Left behind what, exactly? I’m quite sure that if someone finds a real productivity metric for the software development, that takes into account speed of execution, technical debt, maintainability, and cost - LLMs won’t shine, and their introduction and usage without brain is just the generation of endless technical debt.

Still, I find myself using LLMs locally because they are a tool, and as I did for any other tool and technology I discovered and used over the years, I just want to use them and master them. But this time the usage is really boring… Luckily in this process, at least, I had some fun setting up a machine for local inference.

What’s the point?

The point was never just going from A to B. The point has always been to have fun while going from A to B, learn something new along the way and gain savvy.

That’s the core issue: savvy. And I am losing it.

According to Merriam-Webster:

savvy: having or showing perception, comprehension, or shrewdness especially in practical matters.

Savvy is the fundamental idea for me - learning by doing. Do, make mistakes, learn from them, repeat, and grow.

With the LLMs every time there’s a mistake, I don’t learn. I can spot the mistake because I have some experience (lucky enough to have been born 34 years ago), I can pinpoint the mistake and let the LLM fix it.

The shitty thing here is that LLM providers can steal my savviness to train their models - so I’m not even going to see those mistakes anymore, and I can’t keep my savviness trained.

So… what now?

I don’t know. In the process of using LLMs I’m still learning new things and having some fun — at least I set up a Linux machine for local inference, and that part was a blast. But this blog post is mainly just a rant, humanly written, that’s going to be used by some company to train some LLM and in the future used by someone else to generate something similar, when prompted “Write me an article that targets the niche of software engineers that are bored by the AI usage. Do not make mistakes”.


联系我们 contact @ memedata.com