7 ms·

They lied to you. Building software is hard

143 points by xiaohanyu 8mo ago

xiaohanyu 8mo ago
"If you are looking for that one trick that lets you get ahead and jumpstart your career, my advice to you is: Don’t choose the path of least resistance. When training a muscle, you only get stronger with resistance. The same is true for learning any new skill. It is when you struggle with a specific problem or concept that you tend to remember." Pretty nice description.
- advisedwang 8mo ago
  As with anything, there's also too much of a good thing though. In my own career I switched role to get more time on a area where I felt I needed more growth an practice. Turns out I never got really very good at it, and basically was just in a role I wasn't great at for 6 years. It was miserable. My lesson is "if you know you are bad at something, don't make it load-bearer in your life or career".
  - hobs 8mo ago
    There's a reason that one of the big corporate skills books is Strength Finder - because fundamentally playing to your weaknesses isn't a good play, its that you need to consistently challenge yourself to keep building whatever muscle you choose to do. You don't want to build strength by lifting 10,000 pounds all at once, but by increasing your load every day. In most professions barely anyone is doing the continual education or paying attention to the "scene" for that profession, if you do that alone you're probably already in the top 10%.
    - Joel_Mckay 8mo ago
      "A Specialist knows more and more about less and less until he knows absolutely everything about nothing. A Generalist knows less and less about more and more until he knows absolutely nothing about everything" Getting paid well doing something you actually enjoy doing is key =3 https://stevelegler.com/2019/02/16/ikigai-a-four-circle-model-of-human-capital/ https://stevelegler.com/2019/02/16/ikigai-a-four-circle-mode...
1970-01-01 8mo ago
We have hard evidence of it becoming easier every damn day. AI is taking these jobs. The models aren't perfect, but the speed tradeoff is so massive that you really can't say it's "hard" to build anything anymore. Nobody is lying.
- contagiousflow 8mo ago
  What is the hard evidence? Edit: What I mean by this is there may be some circumstantial evidence (less hiring for juniors, more AI companies getting VC funding). We currently have no _hard_ evidence that programming has had a substantial speed increase/deskilling from LLMs yet. Any actual __science__ on this has yet to show this. But please, if you have _hard_ evidence on this topic I would love to see it.
  - fullshark 8mo ago
    Closest I guess is hiring of juniors is down, but it's possibly just due to a post COVID pullback being credited to AI. I definitely think a lot of junior tasks are being replaced with AI, and companies are deciding it's not worth filling junior roles at least temporarily as a result.
    - chasd00 8mo ago
      > I definitely think a lot of junior tasks are being replaced with AI I think team expansion is being reduced as well. If you took a dev team of 5, armed them all with Claude Code + training on where to use it and where not to I think you could get the same productivity as hiring 2 additional FTE software devs. I'm assuming your existing 5 devs fully adopt the tool and not reject it like a bad organ transplant. Maybe an analogy could be the invention of email reducing the need for corporate typing pools and therefore fewer jr. secretaries ( typists) are hired. /i'm just guessing that being a secretary is in the career progression path of someone in the typing pool but you get the idea. edit: one thing i missed in my email analogy is that when email was invented it was free and available to anyone that could set up sendmail/*.MTA
    - datsci_est_2015 8mo ago
coffeefirst 8mo ago
One more thing… The newbie prototype was never all that hard. You could, in my day, have a lot of fun that first week with dreamweaver, Visual Basic, or cargo cutting HTML. There’s nothing wrong with this. But to get much further than that ceiling you probably needed to crack a book.
stronglikedan 8mo ago
Building software is actually so easy that my 8 year old niece can do it. Shipping software is what's hard.
- giancarlostoro 8mo ago
  Shipping is easy, shipping stable functional (lets lump in scalable) software on the other hand.
  - 9rx 8mo ago
    Also easy. The only hard thing found around software is the people.
    - acedTrex 8mo ago
      This is true, its easy to ship software to 0 users.
- enos_feedler 8mo ago
  But who are you shipping it to if everyone is building it?
- camnora 8mo ago
  Not to mention selling it
- catoc 8mo ago
  I understand the “8 year old niece” is hyperbole, but really? Everyone can build apps? “Build me a recipe app”, sure. Building anything substantial has consistently failed for me unless you take claude or codex by the hand and guide them through it step by step.
citelao 8mo ago
Perhaps this is a bit OT, since the article focuses more on self-development ("When training a muscle, you only get stronger with resistance"), but I wonder about the subtitle: > Every week there seems to be a new tool that promises to let anyone build applications 10x faster. The promise is always the same and so is the outcome. Is the second sentence true? Regardless of AI, I think that programming (game development, web development, maybe app development) is easier than ever? Compare modern languages like Go & Rust to C & C++, simply for their ease-of-compilation and execution. Compare modern C# to early C#, or modern Java to early Java, even. I'd like to think that our tools have made things easier, even if our software has gotten commensurately more complicated. If they haven't, what's missing? How can we build better tools for ourselves?
- drdec 8mo ago
  Maybe the outcome they had in mind was "it helps, but nowhere near 10x"? Also, I'm not sure anyone was making 10x claims about the tools you cite.
- wrs 8mo ago
  You missed the word "anyone". Of course tools for programmers have seen huge improvements. The "promise" referred to here is that you don't need to learn programming skills to be an effective programmer.
- dijit 8mo ago
  I'm not sure. Think of the Game hits from the 90's. A room full of people made games which shaped a generation. Maybe it was orders of magnitude harder then, but today, it's multiple orders of magnitude more people required to make them. Same is true for websites. Sure, the websites were dingy with poor UX and oodles of bugs... but the size of the team required to make them was absolutely tiny compared to today. Things are simultaneously the best they've ever been, and the worst they've ever been, it's a weird situation to be in for sure. But truthfully; orders of magnitude more powerful hardware was the real unlock. Why is slack and discord popular? Because it's possible to use multiple gigabytes of ram for a chat client. 25 years ago? Multiple gigabytes of ram put your machine firmly in the "I have unlimited money and am probably a server doing millions of things" class.
didgetmaster 8mo ago
Anything (software or physical things) that is fast, easy, and cheap to build; will never be a financial success for a single company. The minute you get some market traction, your competitors will come in and take away all your customers.
- charcircuit 8mo ago
  If you were given a copy of the entire software stack that runs YouTube I would bet $1000000 you can't take all of YouTube's customers. Businesses are more than just the software.
  - didgetmaster 8mo ago
    Are you saying that you think that YouTube was fast, easy, and cheap to build?
    - charcircuit 8mo ago
      As AI progresses it will be. What makes YouTube valuable is the company's relationship with advertisers, content creators, and users.
yowlingcat 8mo ago
Something that's been on my mind recently - what if gen AI coding tools are ultimately attention casinos in the same way social media is? You burn through tons of tokens and you pay per token, it feels productive and engaging, but ultimately the more you try and fail, the more money the vendor makes. Their expressed (though perhaps not stated) economic goal may be to keep you in the "goldilocks zone" of making enough progress to not give up, but not so much progress that you 1-shot to the end state without issues. I'm not saying that they can actually do that per sé; switching costs are so low that if you are doing worse than an existing competitor, you'd lose that volume. Nor am I saying they are deliberately bilking folks -- I think it would be hard to do that without folks cottoning on. But, I did see an interesting thread on Twitter that had me pondering [1]. Basically, Claude Code experimented with RAG approaches over the simple iterative grep that they now use. The RAG approach was brittle and hard to get right in their words, and just brute forcing it with grep was easier to use effectively. But Cursor took the other approach to make semantic searching work for them, which made me wonder about the intrinsic token economics for both firms. Cursor is incentivized to minimize token usage to increase spread from their fixed seat pricing. But for Claude, iterative grep bloating token usage doesn't harm them and in fact increases gross tokens purchased, so there is no incentive to find a better approach. I am sure there are many instances of this out there, but it does make me inclined to wonder if it will be economic incentives rather than technical limitations that eventually put an upper limit on closed weight LLM vendors like OpenAI and Claude. Too early to tell for now, IMO. [1] https://x.com/antoine_chaffin/status/2018069651532787936 https://x.com/antoine_chaffin/status/2018069651532787936
- Throaway1985232 8mo ago
  Well, the first time i got really excited about an LlM was when it told me “yes, if you give me your game ideas and we iterate together, i can handle 100% of the coding.” lies, pure lies.
tom2948329494 8mo ago
> The problem is that while these tools can help you build a simple prototype incredibly quickly, when it comes to building functional applications they are much more limited As someone with 0 (zero) swift skills and who has built a very well functioning iOS app purely with AI, I disagree. AI made me infinitly faster because without it I wouldn‘t even have tried to build it. And yes, I know the limits and security concerns and understand enough to be effective with AI. You can build functioning applications just fine. It‘s complexity and novel problems where AI _might_ struggle, but not every software is complex or novel.
- anonymous344 8mo ago
  do you make money with it? like monthly subscription? because that's my achilles heel, how to synch the mysql backend with apple's payment system so it knows when user ordered or cancelled
  - tom2948329494 8mo ago
    Yes, using apples own storekit for in-app purchases.
anonymous344 8mo ago
true. I've built a simple app that solved the annoying problem usually in that app-space of giving/typing time and date. after years and years, people still pay for it, which im very grateful. i even saw many M$ companies build their products yet lacked the simple mind to ease the user ecperience with non-default date and time selector..
Tiberium 8mo ago
Am I missing something or is the actual point of the article just "don't start learning programming by using AI"? The title seems very different from the content.
worik 8mo ago
> They make the simple parts of software development simpler, but the complex parts can often become more difficult. This is so frustratingly common.
dfabulich 8mo ago
This article includes a graph with a negative slope, claiming that AI tools are useful for beginners, but less and less useful the more coding expertise you develop. That doesn't match my experience. I think AI tools have their own skill curve, independent of the skill curve of "reading/writing good code." If you figure out how to use the AI tools well, you'll get even more value out of them with expertise. Use AI to solve problems you know how to solve, not problems that are beyond your understanding. (In that case, use the AI to increase your understanding instead.) Use the very newest/best LLM models. Make the AI use automated tests (preferring languages with strict type checks). Give it access to logs. Manage context tokens effectively (they all get dumber the more tokens in context). Write the right stuff and not the wrong stuff in AGENTS.md.
- PaulRobinson 8mo ago
  That sounds exhausting. I'd rather spend my time thinking about the problem and solving it, than thinking about how to get some software to stochasticaly select language that appears like it is thinking about the problem to then implement a solution I'm going to have to check carefully. Much of the LLM hype cycle breaks down into "anyone can create software now", which TFA makes a convincing argument for being a lie, and "experts are now going to be so much more productive", which TFA - and several studies posted here in recent months - show is not actually the case. Your walk-through is the reason why. You've not got magic for free, you've got something kinda cool that needs operational management and constant verification.
  - Throaway1985232 8mo ago
    I’ve seen otherwise intelligent and capable people get so addicted to the convenience and potential of LLMs, that they start to lose their ability to slowly go through problems step by step. it’s sad.
  - sgarland 8mo ago
    Agreed. My work is mandating Claude Code usage this week for everyone. I spent all day today getting it to write tickets, code, and tests for something I knew how to do. I don’t understand the appeal. Telling the AI “commit those changes and then push,” then waiting for the result, takes way longer than gcmsg <commit msg> && gp.
raincole 8mo ago
I only realize how spot on the muscle training analogy is. In the modern world, very few people are hired for their muscles alone. Actually building muscle costs money for the absolute majority. This is how I see hand-building software goes.
gdubs 8mo ago
One of my all-time favorite quotes is from Zen Mind, Beginner's Mind and it goes: “In the beginner’s mind there are many possibilities, but in the expert’s there are few.” There's such a wide divergence of experience with these tools. Often times people will say that anyone finding incredible value in them must not be very good. Or that they fall down when you get deep enough into a project. I think the reality is that to really understand these tools, you need to open your mind to a different way of working than we've all become accustomed to. I say this as someone who's made a lot of software, for a long time now. (Quite successfully too!) In someways, while the ladder may be getting pulled up on Junior developers, I think they're also poised to be able to really utilize these tools in a way that those of us with older, more rigid ways of thinking about software development might miss.
- phicoh 8mo ago
  There have always been young people who can quickly hack something together with whatever new tools are available. That way of working never lasts, but the tools do last. When tools prove their worth, they get taken into to normal way software is produced. Older people start using them, because they see the benefit. The key thing about software production is that it is a discussion among humans. The computer is there to help. During a review, nobody is going to look at what assembly a compiler produces (with some exceptions of course). When new tools arrive, we have to be able to blindly trust them to be correct. They have to produce reproducible output. And when they do, the input to those tools can become part of the conversation among humans. (I'm ignoring editors and IDEs here for the moment, because they don't have much effect on design, they just make coding a bit easier). In the past, some tools have been introduced, got hyped, and faded into obscurity again. Not all tools are successful, time will tell.
- commandlinefan 8mo ago
  ... and the biggest problem is that the people who _do_ know how hard it is to build software are the ones whose input on the matter is most likely to be discounted as "sour grapes"/"fear of obsolescence".
  - pixl97 8mo ago
adam_arthur 8mo ago
LLMs have clearly accelerated development for the most skilled developers. Particularly when the human acts as the router/architect. However, I've found Claude Code and Co only really work well for bootstrapping projects. If you largely accept their edits unchanged, your codebase will accrue massive technical debt over time and ultimately slow you down vs semi-automatic LLM use. It will probably change once the approach to large scale design gets more formalized and structured. We ultimately need optimized DSLs and aggressive use of stateless sub-modules/abstractions that can be implemented in isolation to minimize the amount of context required for any one LLM invocation. Yes, AI will one shot crappy static sites. And you can vibe code up to some level of complexity before it falls apart or slows dramatically.
- athenot 8mo ago
  > We ultimately need optimized DSLs and aggressive use of stateless sub-modules/abstractions that can be implemented in isolation to minimize the amount of context required for any one LLM invocation. Containment of state also happens to benefit human developers too, and keep complexity from exploding.
  - adam_arthur 8mo ago
    Yes! I've found the same principles that apply to humans apply to LLMs as well. Just that the agentic loops in these tools aren't (currently) structured and specific enough in their approach to optimally bound abstractions. At the highest level, most applications can be written in simple, plain english (expressed via function names). Both humans and LLMs will understand programs much better when represented this way
- lowbloodsugar 8mo ago
  >If you largely accept their edits unchanged, your codebase will accrue massive technical debt over time and ultimately slow you down vs semi-automatic LLM use. Worse, as its planning the next change, it's reading all this bad code that it wrote before, but now that bad code is blessed input. It writes more of it, and instructions to use a better approach are outweighed by the "evidence". Also, it's not tech debt: https://news.ycombinator.com/item?id=27990979#28010192 https://news.ycombinator.com/item?id=27990979#28010192
countWSS 8mo ago
There is a point in there, long-range analysis and debugging without AI is much harder, AI spots lots of non-obvious stuff very fast. If we consider "spotting non-obvious flaws" a skill, this will atrophy as beginners will learn to use AI to scan code for flaws,it is effective but doesn't teach anything, reading long blocks of code and mentally simulating it is a incredibly valuable skill and it will find stuff AI misses(something that is too complex, e.g. nested/recursive control flow,async and co-routines/threads interacting,etc), AI goes for obvious stuff first and has to be manually pointed to "identify flaws, focusing on X".
mlsu 8mo ago
Fred Brooks, from "No Silver Bullet" (1986) > All software construction involves essential tasks, the fashioning of the complex conceptual structures that compose the abstract software entity, and accidental tasks, the representation of these abstract entities in programming languages and the mapping of these onto machine languages within space and speed constraints. Most of the big past gains in software productivity have come from removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time. How much of what software engineers now do is still devoted to the accidental, as opposed to the essential? Unless it is more than 9/10 of all effort, shrinking all the accidental activities to zero time will not give an order of magnitude improvement. AI, the silver bullet. We just never learn, do we?
- raincole 8mo ago
  I think software was indeed 9/10 accidental activities before AI. Probably still mostly accidental activities with the current LLM. The essence: query all the users within a certain area and do it as fast as possible The accident: spending an hour to survey spatial tree library, another hour debating whether to make our own, one more hour reading the algorithm, a few hours to code it, a few days to test and debug it Many people seem to believe implementing the algorithm is "the essence" of software development so they think the essence is the majority. I strongly disagree. Knowing and writing the specific algorithm is purely accidental in my opinion.
  - etamponi 8mo ago
    It that's the essence, then of course 9/10 is accident. I think that's not software engineering though. The essence: I need to make this software meet all the current requirements while making it easy to modify in the future. The accident: ? Said another way: everyone agrees that LLMs make it very easy to build throw away code and prototypes. I could build these kind of things when I was 15, when I still was on a 56k internet connection and I only knew a bit of C and html. But that's not what software engineers (even junior software engineers) need to do.
  - idle_zealot 8mo ago
zkmon 8mo ago
> With no-code tools you often reach a hard limit where the tool simply does not make sense to use anymore. No-code is the same trend that has abstracted out all the generic stuff into infrastructure layers, letting the developers to focus on Lambda functions, while everything in the lower levels is config-driven. This was happening all the time, pushing the developer to easier higher layers and absorbing all complexity and algorithmic work into config-driven layers. Runtime cost of a Lambda function might far exceed that of a fully hand-coded application hosted on your local server. But there could be other factors to consider. Same with AI. You get a jump-start with full speed, and then you can take the wheel.
- etamponi 8mo ago
  The point of the article is that the jump start that AI gives you is not the same as the one that well thought frameworks give you. What AI writes falls apart and leaves you with the ruins.
threethirtytwo 8mo ago
Software is the hardest thing on planet earth. That's why there's this concept of bootcamps. No other profession has this concept of "bootcamps". Building a plane is easier than building software. That's why they don't have bootcamps for building planes or becoming a rocket engineer. Building rockets or planes as an engineer is a breeze so there's no point in making a bootcamp. That's the awesome thing about being a swe, it's so hard that it's beyond getting a university degree, beyond requiring higher math to learn. Basically the only way to digest the concept of software is to look at these "tutorials" on the internet or have AI vibe code the whole thing (which shows how incredibly hard it is, just ask chatGPT). My friend became a rocket engineer and he had to learn calculus, physics and all that easy stuff which university just transferred into his brain in a snap. He didn't have to go through an internet tutorial or bootcamp.
13415 8mo ago
I have to strongly disagree with that. I provably never was as productive as when I used REALbasic, which was a classical RAD tool. I sold the software made with it successfully for quite a while. As most people here probably know, it's now called Xojo and in my opinion both somewhat outdated and expensive. So I'm not recommending it, but credit to were it's due and it certainly was due for early versions of REALbasic when it was still affordable shareware. The problem with all RAD tools seems to be that they eventually morph into expensive corporate tools no matter what their origins were. I don't know any cross-platform exception (I don't count Purebasic as RAD and it's also not structured). As for AI, it seems to be just the same. The right AI tool accelerates the easy parts so you have more time for the hard parts. Another thing that bothers me a lot when alleged "professionals" are arguing against everyday computing for everyone. They're accelerating the death of general computing platforms and in the end no one will benefit from that.
sumanep 8mo ago
Who lied to me?
vineethy 8mo ago
strongly disagree with this article. I think using the tools can actually directly lead to a junior engineer getting closer to a senior engineer. Telling junior engineers that they have to get better at typing out code in order to be better engineers misses what actually makes someone a better engineer. It's worth actually being specific about what differentiates a junior engineer from a senior engineer. There's two things: communication and architecture. the combination of these two makes you a better problem solver. talking to other people helps you figure out your blindspots and forces you to reduce complex ideas down to their most essential parts. the loop of solving a problem and then seeing how well the solution worked gives you an instinct for what works and what doesn't work for any given problem. So how do agents make you better at these two things? If you are better at explaining what you want, you can get the agents to do what you want a lot better. So you'd end up being more productive. I've seen junior developers that were pretty good problem solvers improve their ability to communicate technical ideas after using agents. Senior engineers develop instincts for issues down the road. So when they begin any project, they'll take this into account and work by thinking through this. They can get the agents to build towards a clean architecture from the get go such that issues are easily traceable and debuggable. Junior developers get better at architecture by using agents because they can quickly churn through candidate solutions. this helps them more rapidly learn the strengths and weaknesses of different architectures.
- lowbloodsugar 8mo ago
  What comes to mind is Java vs assembly. Claude is just a really really high level language compiler. I work with senior Java devs who have never written assembly. On the learning front, I spend the weekend asking Claude questions about Rust, and then getting it to write code that achieved the result I wanted. I also now have a much better understanding of the different options because I've gotten three different working examples and gotten to tinker with them. It's a lot faster to learn how an engine works when you have a working engine on a dyno than when you have no engine. Claude built me a diesel, a gasoline and an electric engine and then I took them apart.
- Thanemate 8mo ago
  People don't develop the ability to solve algebraic equations when they see a professor solving it on the whiteboard. That's just the introduction to the methodology. The way people develop problem solving is by solving problems themselves. This is why everyone's thirsty for senior/staff engineers who are AI powered right now, because their entire work experience was the typical SWE experience. I cannot wait for the industry to have a highly skilled SWE drought in the next 5 years, so I can sweep in and become the AI powered engineer who saves the day because other junior-mid SWE's outsourced their problem solving way too early, either due to falling for the "don't be left behind" narrative (which is absurd because what about people who will get into CS in 6 years from now? Do they miss some metaphorical train?) or because their manager forced them to adopt the tools.
ElijahLynn 8mo ago
I read a quote from somebody in the industry recently that stuck. I don't remember who it was. "Writing software is easy, changing it is hard."
- ryandvm 8mo ago
  Absolutely true. Especially so with poorly abstracted software design. This is why so many new teams' first order of business is invariably a suggestion to "rewrite everything". They're not going to do a better job or get a better product, it's just the only way they're going to get a software stack that does what they want.
Bishonen88 8mo ago
https://www.youtube.com/watch?v=7lzx9ft7uMw https://www.youtube.com/watch?v=7lzx9ft7uMw ^ Everything App for Personal use that I'm thinking about making public in some way ~50k loc with ~400 files. Docker, postgres, react + fastify I'd say between 15 and 20 hours of vibe coding - Tasks, Goals, Habits - Calendar showing all of the above with two way google sync - Household sharing of markdown notes, goals and more - Financial projections, spending, earning, recurring transactions and more - Meal tracking with pics, last eaten, star rating and more - Gantt chart for goals - Dashboard for at a glance view - PWA for android with layout optimizations - Dark mode ... and more Could've I done it in the last 5 years? Yes. It would've taken 3-4 months if not more though. Now we could talk 24/7 about whether it's clean code, super maintainable, etc. etc. The code written by hand wouldn't be either if it'd be me just doing a hobby project. Shipping is rather straightforward as well thanks to LLM's. They hold your hand most of the way. Being a techie makes this much, much easier... I think developers are cooked one way or another. Won't take long now. Same question asked a year ago was dramatically different. AI were helpful to some extent but couldn't code up basic things.
jackinthehat 8mo ago
years ago I watched a very senior engineer refuse to use an IDE debugger because “real understanding means doing it in your head.” He was brilliant - and also spent two days chasing a bug a junior fixed in 10 minutes by setting a breakpoint. The junior didn’t understand more; he just had a better tool for that moment. Tools don’t make you wiser or lazier by default — they amplify whatever habits you already have. If you’re using them to avoid thinking, that shows. If you’re using them to explore faster, that shows too. Beginner’s mind isn’t about ignorance; it’s about being willing to try leverage where it exists.
prewett 8mo ago
I wonder if 3D printing is a good analogy. The promise was "you can print anything you want!" From my observation, the reality is that you can 3D print cheap plastic crap that looks like voxel rendering made manifest. This turns out to be handy in a lot of situations, like making custom jigs for something, but you're not going to be 3D printing custom jewelry, or custom furniture. Sure, you hear stories about how SpaceX is 3D printing rocket engines, but you can't afford a machine like that, and even if you could, you won't be printing custom jewelry with it. So, sure, some people are going to be using AI to create professional software, but they aren't going to tell you about all the engines that blew up along the way, and who knows which ones are going to blow up in the future. But custom utility software might get a whole lot more common.

143 points by xiaohanyu 8mo ago

"If you are looking for that one trick that lets you get ahead and jumpstart your career, my advice to you is: Don’t choose the path of least resistance. When training a muscle, you only get stronger with resistance. The same is true for learning any new skill. It is when you struggle with a specific problem or concept that you tend to remember." Pretty nice description.

advisedwang 8mo ago
As with anything, there's also too much of a good thing though. In my own career I switched role to get more time on a area where I felt I needed more growth an practice. Turns out I never got really very good at it, and basically was just in a role I wasn't great at for 6 years. It was miserable. My lesson is "if you know you are bad at something, don't make it load-bearer in your life or career".
- hobs 8mo ago
  There's a reason that one of the big corporate skills books is Strength Finder - because fundamentally playing to your weaknesses isn't a good play, its that you need to consistently challenge yourself to keep building whatever muscle you choose to do. You don't want to build strength by lifting 10,000 pounds all at once, but by increasing your load every day. In most professions barely anyone is doing the continual education or paying attention to the "scene" for that profession, if you do that alone you're probably already in the top 10%.
  - Joel_Mckay 8mo ago
    "A Specialist knows more and more about less and less until he knows absolutely everything about nothing. A Generalist knows less and less about more and more until he knows absolutely nothing about everything" Getting paid well doing something you actually enjoy doing is key =3 https://stevelegler.com/2019/02/16/ikigai-a-four-circle-model-of-human-capital/ https://stevelegler.com/2019/02/16/ikigai-a-four-circle-mode...

1970-01-01 8mo ago

We have hard evidence of it becoming easier every damn day. AI is taking these jobs. The models aren't perfect, but the speed tradeoff is so massive that you really can't say it's "hard" to build anything anymore. Nobody is lying.

contagiousflow 8mo ago
What is the hard evidence? Edit: What I mean by this is there may be some circumstantial evidence (less hiring for juniors, more AI companies getting VC funding). We currently have no _hard_ evidence that programming has had a substantial speed increase/deskilling from LLMs yet. Any actual __science__ on this has yet to show this. But please, if you have _hard_ evidence on this topic I would love to see it.
- fullshark 8mo ago
  Closest I guess is hiring of juniors is down, but it's possibly just due to a post COVID pullback being credited to AI. I definitely think a lot of junior tasks are being replaced with AI, and companies are deciding it's not worth filling junior roles at least temporarily as a result.
  - chasd00 8mo ago
    > I definitely think a lot of junior tasks are being replaced with AI I think team expansion is being reduced as well. If you took a dev team of 5, armed them all with Claude Code + training on where to use it and where not to I think you could get the same productivity as hiring 2 additional FTE software devs. I'm assuming your existing 5 devs fully adopt the tool and not reject it like a bad organ transplant. Maybe an analogy could be the invention of email reducing the need for corporate typing pools and therefore fewer jr. secretaries ( typists) are hired. /i'm just guessing that being a secretary is in the career progression path of someone in the typing pool but you get the idea. edit: one thing i missed in my email analogy is that when email was invented it was free and available to anyone that could set up sendmail/*.MTA
  - datsci_est_2015 8mo ago

coffeefirst 8mo ago

One more thing… The newbie prototype was never all that hard. You could, in my day, have a lot of fun that first week with dreamweaver, Visual Basic, or cargo cutting HTML. There’s nothing wrong with this. But to get much further than that ceiling you probably needed to crack a book.

stronglikedan 8mo ago

Building software is actually so easy that my 8 year old niece can do it. Shipping software is what's hard.

giancarlostoro 8mo ago
Shipping is easy, shipping stable functional (lets lump in scalable) software on the other hand.
- 9rx 8mo ago
  Also easy. The only hard thing found around software is the people.
  - acedTrex 8mo ago
    This is true, its easy to ship software to 0 users.
enos_feedler 8mo ago
But who are you shipping it to if everyone is building it?
camnora 8mo ago
Not to mention selling it
catoc 8mo ago
I understand the “8 year old niece” is hyperbole, but really? Everyone can build apps? “Build me a recipe app”, sure. Building anything substantial has consistently failed for me unless you take claude or codex by the hand and guide them through it step by step.

citelao 8mo ago

Perhaps this is a bit OT, since the article focuses more on self-development ("When training a muscle, you only get stronger with resistance"), but I wonder about the subtitle: > Every week there seems to be a new tool that promises to let anyone build applications 10x faster. The promise is always the same and so is the outcome. Is the second sentence true? Regardless of AI, I think that programming (game development, web development, maybe app development) is easier than ever? Compare modern languages like Go & Rust to C & C++, simply for their ease-of-compilation and execution. Compare modern C# to early C#, or modern Java to early Java, even. I'd like to think that our tools have made things easier, even if our software has gotten commensurately more complicated. If they haven't, what's missing? How can we build better tools for ourselves?

drdec 8mo ago
Maybe the outcome they had in mind was "it helps, but nowhere near 10x"? Also, I'm not sure anyone was making 10x claims about the tools you cite.
wrs 8mo ago
You missed the word "anyone". Of course tools for programmers have seen huge improvements. The "promise" referred to here is that you don't need to learn programming skills to be an effective programmer.
dijit 8mo ago
I'm not sure. Think of the Game hits from the 90's. A room full of people made games which shaped a generation. Maybe it was orders of magnitude harder then, but today, it's multiple orders of magnitude more people required to make them. Same is true for websites. Sure, the websites were dingy with poor UX and oodles of bugs... but the size of the team required to make them was absolutely tiny compared to today. Things are simultaneously the best they've ever been, and the worst they've ever been, it's a weird situation to be in for sure. But truthfully; orders of magnitude more powerful hardware was the real unlock. Why is slack and discord popular? Because it's possible to use multiple gigabytes of ram for a chat client. 25 years ago? Multiple gigabytes of ram put your machine firmly in the "I have unlimited money and am probably a server doing millions of things" class.

didgetmaster 8mo ago

Anything (software or physical things) that is fast, easy, and cheap to build; will never be a financial success for a single company. The minute you get some market traction, your competitors will come in and take away all your customers.

charcircuit 8mo ago
If you were given a copy of the entire software stack that runs YouTube I would bet $1000000 you can't take all of YouTube's customers. Businesses are more than just the software.
- didgetmaster 8mo ago
  Are you saying that you think that YouTube was fast, easy, and cheap to build?
  - charcircuit 8mo ago
    As AI progresses it will be. What makes YouTube valuable is the company's relationship with advertisers, content creators, and users.

yowlingcat 8mo ago

Something that's been on my mind recently - what if gen AI coding tools are ultimately attention casinos in the same way social media is? You burn through tons of tokens and you pay per token, it feels productive and engaging, but ultimately the more you try and fail, the more money the vendor makes. Their expressed (though perhaps not stated) economic goal may be to keep you in the "goldilocks zone" of making enough progress to not give up, but not so much progress that you 1-shot to the end state without issues. I'm not saying that they can actually do that per sé; switching costs are so low that if you are doing worse than an existing competitor, you'd lose that volume. Nor am I saying they are deliberately bilking folks -- I think it would be hard to do that without folks cottoning on. But, I did see an interesting thread on Twitter that had me pondering [1]. Basically, Claude Code experimented with RAG approaches over the simple iterative grep that they now use. The RAG approach was brittle and hard to get right in their words, and just brute forcing it with grep was easier to use effectively. But Cursor took the other approach to make semantic searching work for them, which made me wonder about the intrinsic token economics for both firms. Cursor is incentivized to minimize token usage to increase spread from their fixed seat pricing. But for Claude, iterative grep bloating token usage doesn't harm them and in fact increases gross tokens purchased, so there is no incentive to find a better approach. I am sure there are many instances of this out there, but it does make me inclined to wonder if it will be economic incentives rather than technical limitations that eventually put an upper limit on closed weight LLM vendors like OpenAI and Claude. Too early to tell for now, IMO. [1] https://x.com/antoine_chaffin/status/2018069651532787936 https://x.com/antoine_chaffin/status/2018069651532787936

Throaway1985232 8mo ago
Well, the first time i got really excited about an LlM was when it told me “yes, if you give me your game ideas and we iterate together, i can handle 100% of the coding.” lies, pure lies.

tom2948329494 8mo ago

> The problem is that while these tools can help you build a simple prototype incredibly quickly, when it comes to building functional applications they are much more limited As someone with 0 (zero) swift skills and who has built a very well functioning iOS app purely with AI, I disagree. AI made me infinitly faster because without it I wouldn‘t even have tried to build it. And yes, I know the limits and security concerns and understand enough to be effective with AI. You can build functioning applications just fine. It‘s complexity and novel problems where AI _might_ struggle, but not every software is complex or novel.

anonymous344 8mo ago
do you make money with it? like monthly subscription? because that's my achilles heel, how to synch the mysql backend with apple's payment system so it knows when user ordered or cancelled
- tom2948329494 8mo ago
  Yes, using apples own storekit for in-app purchases.

anonymous344 8mo ago

true. I've built a simple app that solved the annoying problem usually in that app-space of giving/typing time and date. after years and years, people still pay for it, which im very grateful. i even saw many M$ companies build their products yet lacked the simple mind to ease the user ecperience with non-default date and time selector..

Tiberium 8mo ago

Am I missing something or is the actual point of the article just "don't start learning programming by using AI"? The title seems very different from the content.

worik 8mo ago

> They make the simple parts of software development simpler, but the complex parts can often become more difficult. This is so frustratingly common.

dfabulich 8mo ago

This article includes a graph with a negative slope, claiming that AI tools are useful for beginners, but less and less useful the more coding expertise you develop. That doesn't match my experience. I think AI tools have their own skill curve, independent of the skill curve of "reading/writing good code." If you figure out how to use the AI tools well, you'll get even more value out of them with expertise. Use AI to solve problems you know how to solve, not problems that are beyond your understanding. (In that case, use the AI to increase your understanding instead.) Use the very newest/best LLM models. Make the AI use automated tests (preferring languages with strict type checks). Give it access to logs. Manage context tokens effectively (they all get dumber the more tokens in context). Write the right stuff and not the wrong stuff in AGENTS.md.

PaulRobinson 8mo ago
That sounds exhausting. I'd rather spend my time thinking about the problem and solving it, than thinking about how to get some software to stochasticaly select language that appears like it is thinking about the problem to then implement a solution I'm going to have to check carefully. Much of the LLM hype cycle breaks down into "anyone can create software now", which TFA makes a convincing argument for being a lie, and "experts are now going to be so much more productive", which TFA - and several studies posted here in recent months - show is not actually the case. Your walk-through is the reason why. You've not got magic for free, you've got something kinda cool that needs operational management and constant verification.
- Throaway1985232 8mo ago
  I’ve seen otherwise intelligent and capable people get so addicted to the convenience and potential of LLMs, that they start to lose their ability to slowly go through problems step by step. it’s sad.
- sgarland 8mo ago
  Agreed. My work is mandating Claude Code usage this week for everyone. I spent all day today getting it to write tickets, code, and tests for something I knew how to do. I don’t understand the appeal. Telling the AI “commit those changes and then push,” then waiting for the result, takes way longer than gcmsg <commit msg> && gp.

raincole 8mo ago

I only realize how spot on the muscle training analogy is. In the modern world, very few people are hired for their muscles alone. Actually building muscle costs money for the absolute majority. This is how I see hand-building software goes.

gdubs 8mo ago

One of my all-time favorite quotes is from Zen Mind, Beginner's Mind and it goes: “In the beginner’s mind there are many possibilities, but in the expert’s there are few.” There's such a wide divergence of experience with these tools. Often times people will say that anyone finding incredible value in them must not be very good. Or that they fall down when you get deep enough into a project. I think the reality is that to really understand these tools, you need to open your mind to a different way of working than we've all become accustomed to. I say this as someone who's made a lot of software, for a long time now. (Quite successfully too!) In someways, while the ladder may be getting pulled up on Junior developers, I think they're also poised to be able to really utilize these tools in a way that those of us with older, more rigid ways of thinking about software development might miss.

phicoh 8mo ago
There have always been young people who can quickly hack something together with whatever new tools are available. That way of working never lasts, but the tools do last. When tools prove their worth, they get taken into to normal way software is produced. Older people start using them, because they see the benefit. The key thing about software production is that it is a discussion among humans. The computer is there to help. During a review, nobody is going to look at what assembly a compiler produces (with some exceptions of course). When new tools arrive, we have to be able to blindly trust them to be correct. They have to produce reproducible output. And when they do, the input to those tools can become part of the conversation among humans. (I'm ignoring editors and IDEs here for the moment, because they don't have much effect on design, they just make coding a bit easier). In the past, some tools have been introduced, got hyped, and faded into obscurity again. Not all tools are successful, time will tell.
commandlinefan 8mo ago
... and the biggest problem is that the people who _do_ know how hard it is to build software are the ones whose input on the matter is most likely to be discounted as "sour grapes"/"fear of obsolescence".
- pixl97 8mo ago

adam_arthur 8mo ago

LLMs have clearly accelerated development for the most skilled developers. Particularly when the human acts as the router/architect. However, I've found Claude Code and Co only really work well for bootstrapping projects. If you largely accept their edits unchanged, your codebase will accrue massive technical debt over time and ultimately slow you down vs semi-automatic LLM use. It will probably change once the approach to large scale design gets more formalized and structured. We ultimately need optimized DSLs and aggressive use of stateless sub-modules/abstractions that can be implemented in isolation to minimize the amount of context required for any one LLM invocation. Yes, AI will one shot crappy static sites. And you can vibe code up to some level of complexity before it falls apart or slows dramatically.

athenot 8mo ago
> We ultimately need optimized DSLs and aggressive use of stateless sub-modules/abstractions that can be implemented in isolation to minimize the amount of context required for any one LLM invocation. Containment of state also happens to benefit human developers too, and keep complexity from exploding.
- adam_arthur 8mo ago
  Yes! I've found the same principles that apply to humans apply to LLMs as well. Just that the agentic loops in these tools aren't (currently) structured and specific enough in their approach to optimally bound abstractions. At the highest level, most applications can be written in simple, plain english (expressed via function names). Both humans and LLMs will understand programs much better when represented this way
lowbloodsugar 8mo ago
>If you largely accept their edits unchanged, your codebase will accrue massive technical debt over time and ultimately slow you down vs semi-automatic LLM use. Worse, as its planning the next change, it's reading all this bad code that it wrote before, but now that bad code is blessed input. It writes more of it, and instructions to use a better approach are outweighed by the "evidence". Also, it's not tech debt: https://news.ycombinator.com/item?id=27990979#28010192 https://news.ycombinator.com/item?id=27990979#28010192

countWSS 8mo ago

There is a point in there, long-range analysis and debugging without AI is much harder, AI spots lots of non-obvious stuff very fast. If we consider "spotting non-obvious flaws" a skill, this will atrophy as beginners will learn to use AI to scan code for flaws,it is effective but doesn't teach anything, reading long blocks of code and mentally simulating it is a incredibly valuable skill and it will find stuff AI misses(something that is too complex, e.g. nested/recursive control flow,async and co-routines/threads interacting,etc), AI goes for obvious stuff first and has to be manually pointed to "identify flaws, focusing on X".

mlsu 8mo ago

Fred Brooks, from "No Silver Bullet" (1986) > All software construction involves essential tasks, the fashioning of the complex conceptual structures that compose the abstract software entity, and accidental tasks, the representation of these abstract entities in programming languages and the mapping of these onto machine languages within space and speed constraints. Most of the big past gains in software productivity have come from removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time. How much of what software engineers now do is still devoted to the accidental, as opposed to the essential? Unless it is more than 9/10 of all effort, shrinking all the accidental activities to zero time will not give an order of magnitude improvement. AI, the silver bullet. We just never learn, do we?

raincole 8mo ago
I think software was indeed 9/10 accidental activities before AI. Probably still mostly accidental activities with the current LLM. The essence: query all the users within a certain area and do it as fast as possible The accident: spending an hour to survey spatial tree library, another hour debating whether to make our own, one more hour reading the algorithm, a few hours to code it, a few days to test and debug it Many people seem to believe implementing the algorithm is "the essence" of software development so they think the essence is the majority. I strongly disagree. Knowing and writing the specific algorithm is purely accidental in my opinion.
- etamponi 8mo ago
  It that's the essence, then of course 9/10 is accident. I think that's not software engineering though. The essence: I need to make this software meet all the current requirements while making it easy to modify in the future. The accident: ? Said another way: everyone agrees that LLMs make it very easy to build throw away code and prototypes. I could build these kind of things when I was 15, when I still was on a 56k internet connection and I only knew a bit of C and html. But that's not what software engineers (even junior software engineers) need to do.
- idle_zealot 8mo ago

zkmon 8mo ago

> With no-code tools you often reach a hard limit where the tool simply does not make sense to use anymore. No-code is the same trend that has abstracted out all the generic stuff into infrastructure layers, letting the developers to focus on Lambda functions, while everything in the lower levels is config-driven. This was happening all the time, pushing the developer to easier higher layers and absorbing all complexity and algorithmic work into config-driven layers. Runtime cost of a Lambda function might far exceed that of a fully hand-coded application hosted on your local server. But there could be other factors to consider. Same with AI. You get a jump-start with full speed, and then you can take the wheel.

etamponi 8mo ago
The point of the article is that the jump start that AI gives you is not the same as the one that well thought frameworks give you. What AI writes falls apart and leaves you with the ruins.

threethirtytwo 8mo ago

Software is the hardest thing on planet earth. That's why there's this concept of bootcamps. No other profession has this concept of "bootcamps". Building a plane is easier than building software. That's why they don't have bootcamps for building planes or becoming a rocket engineer. Building rockets or planes as an engineer is a breeze so there's no point in making a bootcamp. That's the awesome thing about being a swe, it's so hard that it's beyond getting a university degree, beyond requiring higher math to learn. Basically the only way to digest the concept of software is to look at these "tutorials" on the internet or have AI vibe code the whole thing (which shows how incredibly hard it is, just ask chatGPT). My friend became a rocket engineer and he had to learn calculus, physics and all that easy stuff which university just transferred into his brain in a snap. He didn't have to go through an internet tutorial or bootcamp.

13415 8mo ago

I have to strongly disagree with that. I provably never was as productive as when I used REALbasic, which was a classical RAD tool. I sold the software made with it successfully for quite a while. As most people here probably know, it's now called Xojo and in my opinion both somewhat outdated and expensive. So I'm not recommending it, but credit to were it's due and it certainly was due for early versions of REALbasic when it was still affordable shareware. The problem with all RAD tools seems to be that they eventually morph into expensive corporate tools no matter what their origins were. I don't know any cross-platform exception (I don't count Purebasic as RAD and it's also not structured). As for AI, it seems to be just the same. The right AI tool accelerates the easy parts so you have more time for the hard parts. Another thing that bothers me a lot when alleged "professionals" are arguing against everyday computing for everyone. They're accelerating the death of general computing platforms and in the end no one will benefit from that.

sumanep 8mo ago

Who lied to me?

vineethy 8mo ago

strongly disagree with this article. I think using the tools can actually directly lead to a junior engineer getting closer to a senior engineer. Telling junior engineers that they have to get better at typing out code in order to be better engineers misses what actually makes someone a better engineer. It's worth actually being specific about what differentiates a junior engineer from a senior engineer. There's two things: communication and architecture. the combination of these two makes you a better problem solver. talking to other people helps you figure out your blindspots and forces you to reduce complex ideas down to their most essential parts. the loop of solving a problem and then seeing how well the solution worked gives you an instinct for what works and what doesn't work for any given problem. So how do agents make you better at these two things? If you are better at explaining what you want, you can get the agents to do what you want a lot better. So you'd end up being more productive. I've seen junior developers that were pretty good problem solvers improve their ability to communicate technical ideas after using agents. Senior engineers develop instincts for issues down the road. So when they begin any project, they'll take this into account and work by thinking through this. They can get the agents to build towards a clean architecture from the get go such that issues are easily traceable and debuggable. Junior developers get better at architecture by using agents because they can quickly churn through candidate solutions. this helps them more rapidly learn the strengths and weaknesses of different architectures.

lowbloodsugar 8mo ago
What comes to mind is Java vs assembly. Claude is just a really really high level language compiler. I work with senior Java devs who have never written assembly. On the learning front, I spend the weekend asking Claude questions about Rust, and then getting it to write code that achieved the result I wanted. I also now have a much better understanding of the different options because I've gotten three different working examples and gotten to tinker with them. It's a lot faster to learn how an engine works when you have a working engine on a dyno than when you have no engine. Claude built me a diesel, a gasoline and an electric engine and then I took them apart.
Thanemate 8mo ago
People don't develop the ability to solve algebraic equations when they see a professor solving it on the whiteboard. That's just the introduction to the methodology. The way people develop problem solving is by solving problems themselves. This is why everyone's thirsty for senior/staff engineers who are AI powered right now, because their entire work experience was the typical SWE experience. I cannot wait for the industry to have a highly skilled SWE drought in the next 5 years, so I can sweep in and become the AI powered engineer who saves the day because other junior-mid SWE's outsourced their problem solving way too early, either due to falling for the "don't be left behind" narrative (which is absurd because what about people who will get into CS in 6 years from now? Do they miss some metaphorical train?) or because their manager forced them to adopt the tools.

ElijahLynn 8mo ago

I read a quote from somebody in the industry recently that stuck. I don't remember who it was. "Writing software is easy, changing it is hard."

ryandvm 8mo ago
Absolutely true. Especially so with poorly abstracted software design. This is why so many new teams' first order of business is invariably a suggestion to "rewrite everything". They're not going to do a better job or get a better product, it's just the only way they're going to get a software stack that does what they want.

Bishonen88 8mo ago

https://www.youtube.com/watch?v=7lzx9ft7uMw https://www.youtube.com/watch?v=7lzx9ft7uMw ^ Everything App for Personal use that I'm thinking about making public in some way ~50k loc with ~400 files. Docker, postgres, react + fastify I'd say between 15 and 20 hours of vibe coding - Tasks, Goals, Habits - Calendar showing all of the above with two way google sync - Household sharing of markdown notes, goals and more - Financial projections, spending, earning, recurring transactions and more - Meal tracking with pics, last eaten, star rating and more - Gantt chart for goals - Dashboard for at a glance view - PWA for android with layout optimizations - Dark mode ... and more Could've I done it in the last 5 years? Yes. It would've taken 3-4 months if not more though. Now we could talk 24/7 about whether it's clean code, super maintainable, etc. etc. The code written by hand wouldn't be either if it'd be me just doing a hobby project. Shipping is rather straightforward as well thanks to LLM's. They hold your hand most of the way. Being a techie makes this much, much easier... I think developers are cooked one way or another. Won't take long now. Same question asked a year ago was dramatically different. AI were helpful to some extent but couldn't code up basic things.

jackinthehat 8mo ago

years ago I watched a very senior engineer refuse to use an IDE debugger because “real understanding means doing it in your head.” He was brilliant - and also spent two days chasing a bug a junior fixed in 10 minutes by setting a breakpoint. The junior didn’t understand more; he just had a better tool for that moment. Tools don’t make you wiser or lazier by default — they amplify whatever habits you already have. If you’re using them to avoid thinking, that shows. If you’re using them to explore faster, that shows too. Beginner’s mind isn’t about ignorance; it’s about being willing to try leverage where it exists.

prewett 8mo ago

I wonder if 3D printing is a good analogy. The promise was "you can print anything you want!" From my observation, the reality is that you can 3D print cheap plastic crap that looks like voxel rendering made manifest. This turns out to be handy in a lot of situations, like making custom jigs for something, but you're not going to be 3D printing custom jewelry, or custom furniture. Sure, you hear stories about how SpaceX is 3D printing rocket engines, but you can't afford a machine like that, and even if you could, you won't be printing custom jewelry with it. So, sure, some people are going to be using AI to create professional software, but they aren't going to tell you about all the engines that blew up along the way, and who knows which ones are going to blow up in the future. But custom utility software might get a whole lot more common.