llm/5888b8dc-b96e-4444-9c3c-465dde409e92/topic-3-f5b9a79b-f6d9-4bae-b3a1-24c233253660-input.json
You are a comment summarizer. Given a topic and a list of comments tagged with that topic, write a single paragraph summarizing the key points and perspectives expressed in the comments. TOPIC: Experienced vs inexperienced developer AI gains COMMENTS: 1. Something I like about our weird new LLM-assisted world is the number of people I know who are coding again, having mostly stopped as they moved into management roles or lost their personal side project time to becoming parents. AI assistance means you can get something useful done in half an hour, or even while you are doing other stuff. You don't need to carve out 2-4 hours to ramp up any more. If you have significant previous coding experience - even if it's a few years stale - you can drive these things extremely effectively. Especially if you have management experience, quite a lot of which transfers to "managing" coding agents (communicate clearly, set achievable goals, provide all relevant context.) 2. Yes and no. Yes, I recon coding is dead. No, that doesn't mean there's nothing to learn. People like to make comparisons to calculators rendering mental arithmetic obsolete, so here's an anecdote: First year of university, I went to a local store and picked up three items each costing less than £1, the cashier rang up a total of more than £3 (I'd calculated the exact total and pre-prepared the change before reaching the head of the queue, but the exact price of 3 items isn't important enough to remember 20+ years later). The till itself was undoubtedly perfectly executing whatever maths it had been given, I assume the cashier mistyped or double-scanned. As I said, I had the exact total, the fact that I had to explain "three items costing less than £1 each cannot add up to more than £3" to the cashier shows that even this trivial level of mental arithmetic is not universal. I now code with LLMs. They are so much faster than doing it by hand. But if I didn't already have experience of code review, I'd be limited to vibe-coding (by the original definition, not even checking). I've experimented with that to see what the result is, and the result is technical debt building up. I know what to do about that because of my experience with it in the past, and I can guide the LLM through that process, but if I didn't have that experience, the LLM would pile up more and more technical debt and grind the metaphorical motorbike's metaphorical wheels into the metaphorical mud. 3. Exactly. And I was never particularly good at coding, either. Pairings with Gemini to finally figure out how to decompile an old Java app so I can make little changes to my user profile and some action files? That was fun! And I was never going to be able to figure out how to do it on my own. I had tried! 4. Yeah, this is a lot of what I'm doing with LLM code generation these days: I've been there, I've done that, I vaguely know what the right code would look like when I see it. Rather than spend 30-60 minutes refreshing myself to swap the context back into my head, I prompt Claude to generate a thing that I know can be done. Much of the time, it generates basically what I would have written, but faster. Sometimes, better, because it has no concept of boredom or impatience while it produces exhaustive tests or fixes style problems. I review, test, demand refinements, and tweak a few things myself. By the end, I have a working thing and I've gotten a refresher on things anyway. 5. Something happened to me a few years ago. I used to write code professionally and contribute to open source a lot. I was freelancing on other people's projects and contributing to mature projects so I was doing hard work, mostly at a low level (I mean algorithms, performance fixes, small new features, rather than high level project architecture). I was working on an open source contribution for a few days. Something that I struggled with, but I enjoyed the challenge and learned a lot from it. As it happened someone else submitted a PR fixing the same issue around the same time. I wasn't bothered if mine got picked or not, it happens. But I remember looking at how similar both of our contributions were and feeling like we were using our brains as computers, just crunching algorithms and pumping in knowledge to create some technical code that was (at the time) impossible for a computer to create. This stayed with me for a while and I decided that doing this technical algorithm crunching wasn't the best use of my human brain. I was making myself interchangeable with all the other human (and now AI) code crunchers. I should move on to a higher level, either architectural or management. This was a big deal for me because I did love (and still do) deeply understanding algorithms and mathematics. I was extremely fortunate with timing as it was just around one year before AI coding became mainstream but early enough that it wasn't a factor in this shift. Now an AI could probably churn out a decent version of that algorithm in a few minutes. I did move on to open my own business with my partner and haven't written much code in a few years. And when I do now I appreciate that I can focus on the high level stuff and create something that my business needs in a few hours without exhausting myself on low level algorithm crunching. This isn't meant to put down the enjoyment of writing code for code's sake. I still do appreciate well written code and the craft that goes into it. I'm just documenting my personal shift and noting that enjoyment can be found on both sides. 6. Ultimately it's up to the user to decide what to do with his time ; it's still a good bargain that leaves a lot of sovereignty to the user. I like to code a little too much ; got into deep tech to capacities I couldn't imagine before - but at some point you hit rock bottom and you gotta ship something that makes sense. I'm like a really technical "predator" - in a sense where to be honest with myself - it has almost become some way of consumption rather than pure problem solving. For very passionate people it can be difficult to be draw the line between pleasure and work - especially given that we just do what we like in the first place - so all that time feel robbed from us - and from the standpoint of "shipper" who didn't care about it in the first place it feels like freedom. But I'd argue that if anyone wants to jump into technical stuff ; it has never been so openly accessible - you could join some niche slack where some competent programmers were doing great stuff. Today a solo junior can ship you a key-val that is going to be fighting redis in benchmarks. It really is not a time to slack down in my opinion - everything feels already existing and mostly already dealt with. But again - for those who are frustrated with the status-quo ; they will always find something to do. I get you however that this has created a very different space where past acquired skill-sets don't necessarily translate as well today - maybe it's just going to be different to find it's space than it was 10 years ago. I like that the cards have be re-dealt though - it's arguably way more open than the stack-overflow era and pre-ai where knowledge was much more difficult to create. 7. I have nearly two decades of programming experience which is mostly server side. The other day I wanted a quick desktop (Linux) program to chat with an LLM. Found out about Viciane launcher, then chalked out an extension in react (which I have never used) to chat with an LLM using OpenAI compatible API. Antigravity wrote a bare minimum working extension in a single prompt. I didn't even need to research how to write an extension for an app released only three to five months ago. I then used AI assistance to add more features and polish the UI. This was a fun weekend but I would have procrastinated forever without a coding agent. 8. Exactly. What makes it even more odd for me is they are mostly describing doing nothing when using their agents. I see the "providing important context, setting guardrails, orchestration" bits appended, and it seems like the most shallow, narrowest moat one can imagine. Why do people believe this part is any less tractable for future LLMs? Is it because they spent years gaining that experience? Some imagined fuzziness or other hand-waving while muttering something about the nature of "problem spaces"? That is the case for everything the LLMs are toppling at the moment. What is to say some new pre-training magic, post-training trick, or ingenious harness won't come along and drive some precious block of your engineering identity into obsolescence? The bits about 'the future is the product' are even stranger (the present is already the product?). To paraphrase theophite on Bluesky, people seem to believe that if there is a well free for all to draw from, that there will still exist a substantial market willing to pay them to draw from this well. 9. Many of the same skills that we honed by investing that time and effort into being good software developers make us good AI prompters, we simply moved another layer of abstraction up the stack. 10. Respect to you. I ran out of energy to correct people's dated misconceptions. If they want to get left behind, it's not my problem. 11. At some point no-one is going to have to argue about this. I'm guessing a bit here, but my guess is that within 5 years, in 90%+ jobs, if you're not using an AI assistant to code, you're going to be losing out on jobs. At that point, the argument over whether they're crap or not is done. I say this as someone who has been extremely sceptical over their ability to code in deep, complicated scenarios, but lately, claude opus is surprising me. And it will just get better. 12. Yes, I worry about this quite a bit. Obviously nobody knows yet how it will shake out, but what I've been noticing so far is that brand recognition is becoming more important. This is obviously not a good thing for startup yokels like me, but it does provide an opportunity for quality and brand building. The initial creation and generation is indeed much easier now, but testing, identifying, and fixing bugs is still very much a process that takes some investment and effort, even when AI assisted. There is also considerable room for differentiation among user flows and the way people interact with the app. AI is not good at this yet, so the prompter needs to be able to identify and direct these efforts. I've also noticed in some of my projects, even ones shipped into production in a professional environment, there are lots of hard to fix and mostly annoying bugs that just aren't worth it, or that take so much research and debugging effort that we eventually gave up and accepted the downsides. If you give the AI enough guidance to know what to hunt for, it is getting pretty good at finding these things. Often the suggested fix is a terrible idea, but The AI will usually tell you enough about what is wrong that you can use your existing software engineering skills and experience to figure out a good path forward. At that point you can either fix it yourself, or prompt the AI to do it. My success rate doing this is still only at about 50%, but that's half the bugs that we used to live with that we no longer do, which in my opinion has been a huge positive development. 13. I'll also argue that level of skill depends on what one can make in those two days... it's like a mirror. If you don't know what to ask for, it doesn't know what to produce 14. All fair points, I think I agree with your take overall but we might each be focusing on situations involving different levels of capital, time, and skill: I'm imagining situations where AI use brought the barrier down substantially for some entrants, but the barriers still meaningfully exist, while it sounds to me like you're considering the essentially zero barrier case. My Glad example was off the cuff but it still feels apt to me for the case I mean: the barrier for an existing plastic product producer who doesn't already to also produce bags is likely very low, but it's still non zero, while the barrier for a random person is quite high. I feel vibe coding made individual projects much cheaper (sometimes zero) for decent programmers, but it hasn't made my mom start producing programming projects -- the barrier still seems quite high for non technical people. 15. I dunno about the Glad bag analogy, and now I'm not sure that the artist analogy applies either. I think a better analogy (i.e. one that we both agree one) is Excel preadsheets. There are very few "Excel consultants" available that companies hire. You can't make money be providing solutions in Excel because anyone who needs something that can be done in Excel can just do it themselves. It's like if your mum needed to sum income and expenditures for a little side-business: she won't be hiring an excel consultant to do write the formulas into the 4 - 6 cells that contain calculations, she'll simply do it herself. I think vibe coding is going to be the same way in a few years (much faster than spreadsheets took off, btw, which occurred over a period of a decade) - someone who needs a little project management applications isn't going to buy one, they can get one in an hour "for free"[1]. Just about anything you can vibe-code, an office worker with minimal training (the average person in 2026, for example) can vibe-code. The skill barrier to vibe-coding little apps like this is less than the skill barrier for creating Excel workbooks, and yet almost every office worker does it. -------------------------------------------------------------- [1] In much the same way that someone considers creating a new spreadsheet to be free when they already have Excel installed, people are going to regard the output of LLMs "free" because they are already paying the monthly fee for it. 16. Yes! I’ve seen this myself, folks moving back into development after years or decades. 17. Only it’s a bit like me getting back into cooking because I described the dish I want to a trainee cook. 18. The difference is that the head chef can cook very well and could do a better job of the dish than the trainee. 19. Yes, people who were at best average engineers and those that atrophied at their skill through lack of practice seem to be the biggest AI fanboys in my social media. It's telling, isn't it? 20. I'm building an AI agent for Godot, and in paid user testing we found the median speed up time to complete a variety of tasks[0] was 2x. This number was closer to 10x for less experienced engineers [0] tasks included making games from scratch and resolving bugs we put into template projects. There's no perfect tasks to test on, but this seemed sufficient 21. That sounds reasonable to me. AI is best at generating super basic and common code, it will have plenty of training on game templates and simple games. Obviously you cannot generalize that to all software development though. 22. I recently used AI to help build the majority of a small project (database-driven website with search and admin capabilities) and I'd confidently say I was able to build it 3 to 5 times faster with AI. For context, I'm an experienced developer and know how to tweak the AI code when it's wonky and the AI can't be coerced into fixing its mistakes. 23. I’m better at it in the spaces where I deliver value. For me that’s the backend, and I’m building complex backends with simple frontends. Sounds like your expertise is the front end, so you’re gonna be doing stuff that’s beyond me, and beyond what the AI was trained on. I found ways to make the AI solve backend pain points (documentation, tests, boiler plate like integrations). There’s probably spaces where the AI can make your work more productive, or, like my move into the front end, do work that you didn’t do before. 24. > I feel like I can manage the entire stack again - with confidence. By not managing anything? Ignorance is bliss, I guess. I understand it. I've found myself looking at new stacks and tech, not knowing what I didn't know, and wondering where to start. But if you skip these fundamentals of the modern dev cycle, what happens when the LLM fails? 25. Then it fails and the world doesn't end. You fix it or delegate it and move on. Most people aren't working on code for power grids and fighter jets. There's room for failure. This same argument was used by the old timers when younger programmers couldn't code assembly or C on bare metal systems. 26. > Over the past two decades, I’ve worked with a lot of talented people > I’ve seen the good and the bad, and I can iterate from there. A bit of a buried lede, perhaps. Being in the industry for two decades, the definitions and fundamentals can rub off on you, with a little effort. There is a big difference between this and a decidedly non-technical individual without industry experience who sets out to do the same thing. This is not the advertised scenario for LLM vibe-coding. 27. A product manager here. Thanks to AI, I was able to create my own website on Astro. I was so fascinated by web technologies, that I didn't realize when I created not just a website, but a blazing fast website with extensive amount of metadata generation (Json-LD, OG, microformats, Dublin Core, PRISM, RSL 1.0, Highwire Press, FAIR singposting, MODS generation) and so on. Thanks to this pet project, I'm now quite capable as a software architect of websites. And it is really fun! 28. > Over the past two decades, I’ve worked with a lot of talented people: backend developers, frontend developers, marketers, leaders, and more. I can lean on those experiences, fall back on how they did things, and implement their methods with AI. Will that really work? You interacted with the end product, but you don't have the experience and learned lessons that those people had. Are you sure this isn't the LLM reinforcing false confidence? Is the AI providing you with the real thing or a cheap imitation and how can you tell? 29. As someone who always dabbled in code but never was a “real” developer, I’ve found the same thing. I know the concepts, I know good from bad — so all of a sudden I can vibe code things that would have taken me months of studying and debugging and banging my head against the wall. If you’ll forgive a bit of self promotion, I also wrote some brief thoughts on my Adventures In AI Prototyping: https://www.andrew-turnbull.com/adventures-in-ai-prototyping... 30. so is it fun because you had fallen behind and now you think you can fit with the people with more experience? well, I have news for you, the people with experience are also using AI too and they can still produce better and more than you do. 31. God created men, ~~Colt~~ LLMs made them equal... 32. This is probably the best post i've seen about the whole LLM / vibe coding space at least in relation to web dev. Indeed, as the author states, the code / agent often needs some coralling, but if you know all the gotchyas / things to look for, you can focus 100% on the creativity part! Been loving it as well. 33. Agree with this. Like the author, I've been keeping ajour with web development for multiple decades now. If you have deep software knowledge pre-LLM, you are equipped with the intuition and knowledge to judge the output. You can tell the difference between good and bad, if it looks and works the way you want, and you can ask the relevant questions to push the solution to the actual thing that you envisioned in your mind. Without prior software dev experience people may take what the LLM gives them at face value, and that's where the slop comes from imho. 34. >>Starting a new project once felt insurmountable. Now, it feels realistic again. Honestly, this does not give me confidence in anything else you said. If you can't spin up a new project on your own in a few minutes, you may not be equipped to deal with or debug whatever AI spins up for you. >>When AI generates code, I know when it’s good and when it’s not. I’v seen the good and the bad, and I can iterate from there. Even with refinement and back-and-forth prompting, I’m easily 10x more productive Minus a baseline, it's hard to tell what this means. 10x nothing is nothing. How am I supposed to know what 1x is for you, is there a 1x site I can look at to understand what 10x would mean? My overall feeling prior to reading this was "I should hire this guy", and after reading it my overwhelming thought was "eat a dick, you sociopathic self-aggrandizing tool." Moreover, if you have skill which you feel is augmented by these tools, then you may want to lean more heavily on that skill now if you think that the tool itself makes everyone capable of writing the same amazing code you do. Because it sounds like you will be unemployed soon if not already, as a casualty of the nonsense engine you're blogging about and touting. 35. I remember when Hacker News felt smaller. Threads were shorter. Context fit in your head. You could read the linked article, skim the comments, and jump in without feeling like you’d missed a prerequisite course. It probably didn’t feel special at the time, but looking back, it was simpler. The entire conversation space was manageable. If you had a thought, you could express it clearly, hit “reply,” and reasonably expect to be understood. As a single commenter, you could hold the whole discussion in your mind. From article to argument to conclusion. Or at least, it felt that way. I’m probably romanticizing it—but you know what I mean. Now, articles are denser. Domains are deeper. Threads splinter instantly. Someone cites a paper, someone else links a counter-paper, a third person references a decades-old mailing list post, and suddenly the discussion assumes years of background you may or may not have. You’re expected to know the state of the art, the historical context, the common rebuttals, the terminology, and the unwritten norms—while also being concise, charitable, and original. Every field has matured—probably for the better—but it demands deeper domain knowledge just to participate without embarrassing yourself. Over time, I found myself backing out of threads I was genuinely interested in, not because I had nothing to say, but because the cognitive load felt too high. As a solo thinker, it became harder to keep up. > AI has entered the chat. They’re far from perfect, but tools like Claude and ChatGPT gave me something I hadn’t felt in a long time: _leverage_. I can now quickly: - Summarize long articles - Recall prior art - Check whether a take is naïve or already debunked - Clarify my own thinking before posting Suddenly, the background complexity matters a lot less. I can go from “half-formed intuition” to “coherent comment” in minutes instead of abandoning the tab entirely. I can re-enter conversations I would’ve previously skipped. > Oh no, you’re outsourcing thinking—bet it’s all slop! Over the years, I’ve read thousands of great HN comments. Thoughtful ones. Careful ones. People who knew when to hedge, when to cite, when to shut up. That pattern is in my head now. With AI, I can lean on that experience. I can sanity-check tone. I can ask, “Is this fair?” or “What am I missing?” I can stress-test an argument before I inflict it on strangers. When AI suggests something wrong, I know it’s wrong. When it’s good, I recognize why. Iteration is fast. Even with back-and-forth refinement, I’m dramatically more effective at expressing what I already think. The goal hasn’t changed: contribute something useful to the discussion. The bar is still high. But now I have a ladder instead of a sheer wall. There’s mental space for curiosity again. My head isn’t constantly overloaded with “did I miss context?”, “is this a known bad take?”, or “will this derail into pedantry?” I can offload that checking to AI and focus on the _idea_. That leaves room to explore. To ask better questions. To write comments that connect ideas instead of defensively hedging every sentence. To participate for the joy of thinking in public again. It was never about typing comments fast, or winning arguments. It was about engaging with interesting people on interesting problems. Writing was just the interface. And with today’s tools, that interface is finally lighter again. AI really has made commenting on Hacker News fun again. 36. 100% the opposite. LLMs lack high level creativity, wisdom and taste. Being a generalist is how you build these. For example, there's a common core to music, art, food, writing, etc that you don't see until you've gotten good at 3+ aesthetic fields. There are common patterns in different academic disciplines and activities that can supercharge your priors and help you make better decisions. LLMs can "see" these these connections if explicitly prompted with domains and details, but they don't seem to reason with them in mind or lean on them by default. On the other hand, LLMs are being aggressively RL'd by the top 10% of various fields, so single field expertise by some of the best in the world is 100% baked in and the default. Write a concise, engaging paragraph (3-5 sentences) that captures the main ideas, notable perspectives, and overall sentiment of these comments regarding the topic. Focus on the most interesting and representative viewpoints. Do not use bullet points or lists - write flowing prose.
Experienced vs inexperienced developer AI gains
36