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- Hacker News
- Yes, it is. Working on a sega genesis homebrew game. I focus on visuals, dialogue, narrative logic, music, etc., and GPT worries about the rest. It works for me. I couldn't care less if works for others. I'm working on the part I enjoy.
- Anyone that sells you on empowering anything is preying on your pride. Don't be a sucker.by brid
- To make a meathead analogy: you all know the people in the gym who apply the strategy of least resistance by taking shortcuts, skipping the hard parts or following some hype new training regimen while forgetting about the fundamentals. None of those people look like they lift and a lot of them remain in the same shape their entire life.
Same with AI. if you use it to skip all the hard parts you will not grow and mature and waste your potential.
- This same argument could be applied to anything. Layers of abstraction exist for a reason, because at a certain point, we can only deal with so many things at once. We have to be able to delegate "the details" to others -- be that a person, a company, or an AI model.
Does "not getting into the details" mean you have to understand how the GCC compiler works when you write C code? Do you need to be an expert in machine code, or how SSE and pipelined instruction caches work to write your little bit of code? Do you need to understand how Ethernet frames work to write an API route for a web server?
Knowing how these things work can be helpful in a broader sense, and perhaps when encountering weird edge cases or dealing with exotic implementation but are generally not required to get the job done. The details, simply put, don't matter because someone else has already thought through the problem and solved it in a way that is good enough for the vast majority of use cases.
The same goes with AI. It's helpful to know how things work, but as the models continue to get better and better, it doesn't matter. As long as they are trained properly by someone who does know the details, that's a far better place to be than training a million different people on it who will each have their own biases, levels of understanding and misconceptions.
by iamleppert - Your vouching for centralization of knowledge, and even understanding, which I think is a very bad idea. It’s not like the rest of us are becoming specialized at something else - knowledge, in the broad sense, is the foundation of all skills. Without it we have nothing to contributeby fortzi
- I didn't interpret it as an argument about layers of abstraction. You need to be in the detail at the level you're writing (and it's certainly advantageous to know some of the detail of the abstractions below) because you're building abstractions on top of that, because the details subtly change at the base level you're building at, impacting the level that you're building to.
- You're confusing translation and interpretation. A compiler is merely translating your code into a lower-level form. (There is a degree of heuristic-based interpretation that goes into generating good code, but the code's observable behavior is not allowed to change.)
But an LLM has to interpret your words and make judgment calls about the logical details, which is a very different thing. An LLM sufficiently intelligent to make acceptable judgment calls in all situations (acceptable enough to ship to production without human review of those details) would likely be indistinguishable from AGI.
by wavemode - It reminds me of “magic”. I say “expelliamus” and the wand flies out the other guys hand, not his hat, or wallet or the person behind’s sandwiches.
The details always matter. There is a (fairly) good book series where it turns out we are living in the matrix and someone finds a config file and effectively becomes a Wizard, and of course problems ensue with details (like flying by adjusting one’s position one feet up every second is just vibrating a lot)
I think ultimately AI is “do what I mean” and the only reason it looks like it’s working is because AI has read all the same books as us
- > There is a (fairly) good book series where it turns out we are living in the matrix and someone finds a config file and effectively becomes a Wizard, and of course problems ensue with details (like flying by adjusting one’s position one feet up every second is just vibrating a lot)
This is very interesting. Which book series is this?
by lelanthran - But surely at some point you need to stop, right? Most web developers are perfectly capable without understanding the intricacies of the file system or OS, even though the browser sits on top them. Most engineers don’t know assembly, or transistors or electrical engineering. Steve Jobs didn’t know every line of Objective-C that went into OSX. I don’t think Michelangelo collected all his paint materials by hand when painting the Sistine Chapel. In some sense I don’t understand a lot of the panic around AI when the entire career progression of what it means to be a software engineer has simply been to deal with ever-increasing levels of abstraction.by johnfn
- The issue is reliability. I don't need to know the implementation of the database or the lower levels of the network tasks because I trust that they works well. A bug that affects me has a very low chance of occurring and the main risk is me not reading or understanding the docs.
With AI generated systems, no one knows what's going on. Not even the AI. When an issue pops up (which has a very good chance to happen because of the complexity of integrations), debugging it can be like reverse engineering obfuscated assembly code.
by skydhash - > Most web developers are perfectly capable without understanding the intricacies of the file system or OS, even though the browser sits on top them.
This will mess you up if you ever need to store a significant amount of files on the file system directly, or even deal with resource usage for batch processes, or have to dig into non-trivial transaction handling when dealing with DBs without having learnt enough about them, or even the whole domain of SQL and query optimization that ORMs will not protect you from and so many other things.
I don't think you need to know everything, but you definitely need to learn.
> But surely at some point you need to stop, right?
I guess, but typically due to your own lack of curiosity or mental exhaustion, or even limited time to spend on something, rather than the new knowledge and skills not being good to have. At least knowing when AI is heading in the wrong direction for any given domain will save your bacon.
by KronisLV - We're living in the glorious future where software engineers don't have to worry about nitty-gritty stuff like actually making software and can focus on the really important work: administrative and managerial tasks!by bitwize
- AI can do administrative tasks.by bonoboTP
- Yeah, I think the peak expectation for AI is that the user gets to be the manager of a very smart team. But we've all seen that. If the manager is clueless about the technology, the results are disappointing.
- And the team (AI) is really not that smart. Just very fast at pasting and gobling stack overflow and github repos togetherby witx
- This post ignores that verifying something works doesn't require you to fully understand it. This is easily observable in products we use everyday. We don't need to understand them to be able to verify they work correctly. The cost of verification is often cheaper than the cost of production.
It present delegation as absence of power/agency. While I think there is some kernel of truth in here, the claim "it's not empowering to hand off the details" quickly falls apart when we consider real examples. If it were true, it would mean any leader who coordinates an organization isn't empowered through delegation. We know that's false. The general claim doesn't hold at all. Somewhere in there is an argument for engineers losing something in the hand off, but it isn't clearly articulated.
by iepathos - >We don't need to understand them to be able to verify they work correctly
I don't think that's the case and it's becoming more obvious every day. From people who cannot distinguish truth or fiction on online platforms, good code from bad, to insecure software that causes supply chain attacks, to investment vehicles or fake medicine peddled that does not work, the idiotic MBA manager who does not understand the workers they manage and so on. That is why we're living on BS metrics and crappy reviews, because to the people who manage organizations their own workplace is a Blackbox. The critique that "AI" systems are semantically blind token processors is correct, it's just not bothering anyone because it already applied to most modern companies.
When people become passive consumers rather than broadly competent users how something works cannot be evaluated so by definition all they have to go off is appearance, which is why we live in the age of influencers and marketing, because that is now the only factor that actually matters.
by Barrin92 - All details are not created equal.
Some details are boring.
My AI dream (that I’m living happily) is getting to focus on the details that I find interesting and ignoring all the boilerplate details that modern software requires.
- So what? Jobs aren't supposed to be fun. Lots of people depend on those boring details.by breezybottom
- You said what I was thinking. AI can do the things I always found mind numbing and tedious, but because I did it already early in my career, I know when to intervene because the agent is not doing it correctly. I think that is the OP's point.by bogometer
- Boilerplate details mean we've failed as an industry. Why haven't we already got the computer to automate all the boring bits without AI?by inigyou
- You develop a taste as to what details you can skim and what you need to dive on. We are all trained on this due to doing 10000 wax on wax off movements, called a "PR Review". I used to scrutinize. Now I think "yep that bit looks good and tests will catch errors plus I can manually test. This bit over here looks scary will spend time more deeply understanding".
With AI you don't need to understand every line in depth but it does need good judgement to decide which.
by hahahaa - "Code Smell" is the term I use here.
With enough experience you can just glance at a diff and see if it "smells". If it does, you drill down and see if you were right. If not, you trust the process and approve.
(And I'm talking about basic run of the mill code here, if you're doing something that kills people, don't go by feels)
by theshrike79 - I've been vibecoding a ton for the past 9 months, built a bunch of cool little apps for myself with AI, ran experiments, built an entire SDLC on skills, did the agent orchestration harness thing, etc. In the past few weeks I've hit a wall where I'm just tired of it. Each model becomes more independent but also harder to direct in detail. They produce massive, tedious, sloppy text outputs with very little input. They're bad at socializing knowledge and communicating design forks.
The places where I've seen unequivocal wins with AI are repetitive tech debt tasks that apply the same transformation across a large amount of code or refactor under a pre-existing test suite with good coverage. It's great for initial research, brainstorming, and can be good (despite the sycophancy) as a rubber duck conversation partner. I use AI constantly, for work and in my personal time, but we've hit a ceiling where I no longer find it helpful for the models to absorb more of the intellectual labor. They get things wrong more aggressively, and more elaborately. They're inadequately curious. I cannot keep up with the endless bad technical writing, and it makes it harder to spot factual errors and bad reasoning.
Here's what I want: I want AI as an assistant that helps me make decisions, and ensures that I'm in the driver's seat. AI as an over-confident prodigy on speed is what we're getting lately, and it's losing me.
by RGS1811 - They're going to run into the problem we have in music production where the song is 'done,' but the producer can't stop adding magic (sugar, fun sounds, more effects, etc.) and the song becomes an overproduced, over magicked mess.
The developers of chatbot technology have or are about to hit an inflection point where the tool is essentially done, then they're going to add too much sugar trying to make it some sort of god tool where it becomes overly complicated, overly convoluted, with dodgy results and too much magic.
by butlike - > Here's what I want: I want AI as an assistant that helps me make decisions, and ensures that I'm in the driver's seat.
You aren't going to get that. Why would they even offer you that? You're going to be the meatbag peripheral to an AI.
by lelanthran - My experience over the past year has been the exact opposite. I work through a design with the agent, have it research any points I'm not confident on, drill down on details I feel fuzzy on, and then have it do an adversarial critique of it until it stops surfacing defects. By the time I'm setting it loose to do the actual implementation, there's very little left to decide and it's producing pretty tight code. Plus I now have some solid design documents, a decision history, an issues list, and a set of gotchas for the agent to keep in mind in future.
My LLM is an assistant that helps my designs and decision process, implements the final work expertly, and I'm always in the driver's seat. And the development speed is astronomical.
by kstenerud - I've been thinking very similar thoughts. Without going into too much detail, a couple of observations:
When carpenters moved from mostly hand tools to mostly power tools, it didn't take away much of the need to understand the techniques of woodworking, it just made it all faster.
Reading a nonfiction book is very different than reading a book summary, even if (as is often the case), the ideas in the book could have been condensed way down. The point is that actually reading the whole book means you spend far more time engaging with the ideas, and so they become more deeply embedded in your neural network. In things like sports, this is just obvious, in that no one expects someone to become a good soccer player by just reading the rulebook, and obviously more practice is the main way you get better.
by cameldrv - Agree.
Creating products with AI is like creating a tightly packed ball of wool. Then, to try to understand what happened you have to somehow pick a strand and pull it out to have a look. Very difficult with tightly packed wool.
I've run some experiments in the past month that get me closer to being able to work with this opaque bundle.
It's the main thing I think about every day, how to solve this problem.
by jv22222