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AI-assisted coding will change software engineering: hard truths

91 points by pseudolus 2y ago

deleted 2y ago
[deleted]
ilaksh 2y ago
The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. Having said that the article seems to accurately reflect what it's like using the current tools. But how could anyone reasonably expect the situation to be similar 3-5 years down the line? If they just didn't frame it as a prediction then it would make sense.
- rileymat2 2y ago
  > But how could anyone reasonably expect the situation to be similar 3-5 years down the line? We can’t, that’s why we are talking about the current capabilities. I remember using Dragon Natural Dictation software before the year 2000, I was young and it blew my mind away with the possibilities. 27 years later it still has not lived up to my young imagination. (It has gotten a ton better, no doubt, and will continue to with more AI)
  - tessierashpool 2y ago
    spoiler alert: this always happens with new technologies, to some extent. the possibilities that materialize are always an odd subset of the possibilities that people envision when the tech is new. you can get better at predicting that subset by studying economics or the humanities, but randomness also plays a crucial role.
    - mooreds 2y ago
      Or it could be because low hanging fruit is low hanging.
  - ilaksh 2y ago
    AssemblyAI's most recent models are incredible. I think you aren't accurately assessing leading edge STT or how your young self would judge this capability level.
- CharlesW 2y ago
  > The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. Is it possible you missed some of it? The entire last half is dedicated to predicting the future based on an agentic future that's (as the author notes) "a big unknown" as we have this conversation.
  - ilaksh 2y ago
    It's not the entire last half. He does mention agents, but inaccurately says that Devin is the only agentic software engineering tool. But he does not override his initial premise of doubting that things will change significantly from the current uses in the future. It's not really unknown, there are several popular tools, and we can expect them to be more reliable and useful as better models continue to be rolled out. Also if you are writing about the future then you should look beyond next year. Everyone should anticipate that there is a strong possibility that we continue on a similar trajectory of improvement. Therefore it is NOT reasonable to expect that things won't change dramatically in software engineering, because the trajectory is very rapid progress. Within the next 3, 5, maybe 10 years, many existing jobs including software engineering may be replaced by AI. That's the direction we are headed and any article should take the possibility seriously by now.
- swiftcoder 2y ago
  > But how could anyone reasonably expect the situation to be similar 3-5 years down the line? That just depends how optimistic you are about rate of improvement. Folks who think we are on the cusp of AGI predict that improvement will be exponential. Folks who think that Moores law will keep up with training costs predict improvement that is somewhat linear. On the other hand, folks who are looking at the rate of change of current LLMs think we're running out of training data, predict that improvements are more likely to be logarithmic, and we're already in the flattening section of the curve...
  - ilaksh 2y ago
    There are many curves. It's a series of sigmoids. There are many innovations to keep up with the expected constant performance increases and demand. They started running out of data, then curated it, then spent more time on inference. There are many small and large innovations that continue to improve performance as each improvement gets implemented and maximized and the gains flatten out and then increase again. Some are using giant SRAM chips. They will then connect them with optics. When that doesn't keep up with demand, probably memristors or something will be scaled to be the next paradigm. Look at the massive improvements made by the DeepSeek team to the open source SOTA recently.
  - godelski 2y ago
    I believe growth is exponential. The growth of complexity. The author mentions how it can get you 70% of the way there, well Pareto is a bitch. Once the details matter more the difficulty grows evidentially (well power series). What people fail to realize is despite what we've done is incredibly and no easy task, it's "the easy part". And progress is unfortunately constantly like this. Because as we progress we are effectively solving higher order solutions. Solve your Taylor series problems. If you've done up to 4th order and still need 5th, everything previously is (usually) "easy" in comparison. Getting to the 6th is no different, and so on. And of course it gets harder, we made progress! The naive part is to think the future will be as easy as the past looks in retrospect, not as hard as the current state looked a priori.
    - intended 2y ago
- logicchains 2y ago
  >It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve Because it's incredibly difficult for people to accept that the career they put years into and love might not exist in 5-10 years. At the current pace, in a few years we'll have something smarter than o3 while no more expensive than o1. Then all it would take is someone to find a nice way to rig it up with short-term memory and wrap it in something like Devin, and then companies would be able to hire the equivalent of a top 1% remote dev for less than 10% of a current dev salary.
- intended 2y ago
  To be fair - the gap between present capabilities and future capabilities is the issue. Having a system that is accurate a random number of times, is very different than a system that is predictable. There’s just too many use cases where GenAI looks like a great fit, but then you have to have the mental overhead of figuring out if this is the time you lose the lottery. It’s just… the kind of mental overhead I wanted a machine to take away from me, not add to my life.
- PaulDavisThe1st 2y ago
  > It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. If you wrote this about aeroplanes in the mid-1960s, despite the previous 40-50 years of rapid improvement, you'd be wrong. The same would be true for bicycles, and automobiles - despite decades of incremental and noteworthy-for-specific-context improvements, there's been no fundamental changes in these (and many other technologies) that reflects the early progress. Yes, modern cars are safer, more comfortable, more fuel efficient (sometimes), but they get nothing done that wasn't possible with a car from the mid-1950s. Why would you assume that the recent past of LLMs provides an outline of what the future of AI in general (not just LLMs) is going to be?
  - dwaltrip 2y ago
    The question is, are current LLMs more like wooden biplanes or early passenger jets? It feels like we are closet to the wooden biplane era, imo.
    - PaulDavisThe1st 2y ago
      Or are they more like a video of flying that we're watching and saying with glee "we're flying!" ?
- DanHulton 2y ago
  The situation certainly won't be similar 3-5 years down the line, but it also won't necessarily be different in the way that people are predicting. In the section about the 70% problem, the author writes: > The good news? This gap will likely narrow as tools improve. This is not a fact, it's a prediction, an article of faith. It's a common prediction, that these tools will only get better over time, but that's not guaranteed! I think it's likely as well, provided you define "improve" incredibly pedantically, but the unspoken part of this prediction is that the tools will improve _significantly,_ and _that_ part is one I have doubts about. Honestly, I think it's pretty likely that we've just about hit the local maximum of our current techniques - training newer, bigger models is fantastically expensive and doesn't seem to have the same jumps in capability as earlier generations did, and stringing together a bunch of models in an agent produces frankly only modest improvements for the cost increase. I've written about "the 70% problem" before (not by name, though), and my biggest worry there is that you require experience and good judgement to be able to use these tools effectively, and these tools by their nature erode that good judgement and deny you the experience. Juniors won't have to work through "the tough parts" of programming and build up the skills required to understand when an LLM is leading them astray, and even experienced programmers can lose familiarity with their own codebases as they rely on LLMs to provide the "understanding." Think of all the times where you haven't had to interact with a service for months, how long it takes to warm back up to it -- what about when that's _most_ of your code, because LLMs have been preventing you from needing to gain any deep understanding about it? What happens if, in 3-5 years, LLMs don't get significantly better, but we've stopped producing properly capable Intermediate and Senior developers, and even started atrophying the skills of existing Seniors? (Couple this with LLM-using developers who can move mountains in a day to kickstart new projects and get promoted, but leave behind a trashfire of a codebase for more "classically" experienced developers to clean up and maintain at a much slower pace. That's a problem that's _always_ been in the industry, just magnified due to the power of LLMs.)
  - MrMcCall 2y ago
    [flagged]
    - 2y ago
gazchop 2y ago
Different take: AI writes a lot of mediocre code for us so we don't have to and we're impressed at that. But that's not the problem we need to solve. All our programming languages are verbose and stupid. It takes too much effort to solve the problems we do in them.
- asdff 2y ago
  You’d think ai models would be good enough to write code in pure binary. Python is for humans. Take the human out there’s no point in it.
  - gazchop 2y ago
    You think something which gets 70% of stuff right on a very good day when the planets are all aligned can write assembly that works? I have news for you.
    - asdff 2y ago
      I think it sure can. Randomly modify a binary function over a million potential permutations and measure the distribution of output, subset from the distribution the binary function(s) that output ahead of your threshold. Should give you a fast function that does what you want with absolutely indescribable code, which isn’t an issue if it does what you want with reasonable performance.
  - koe123 2y ago
    I suppose the issue is a data problem, there being relatively little high quality data explaining how things should be solved in binary. As such making the learning mapping between prompt (english) and good solution (binary) difficult.
    - ninetyninenine 2y ago
      The data is easily generated by compiling the code.
ilrwbwrkhv 2y ago
I heavily started using windscribe and cursor editor. Mind you, I have 10 years of experience. But lately I have cut down their usage and gone back to writing stuff by hand. Because it is so easy to apply the changes the AI suggests but there's this subtle shift over time in the code base towards a non-optimal architecture. And it becomes really hard to get out of. The place where I still use AI quite a lot is autocomplete but of the intelligent kind like if I am returning a different string for each enum, all of that gets autocompleted really fast. So line completion models like what JetBrains provides for free is I think the right balance. Supermaven also works well.
- 8s2ngy 2y ago
  I am in the same boat with you. I have also come to dislike having AI code generator inside my editor. Recently, I started looking into learning Rust seriously and decided to go with RustRover (the rust IDE from JetBrains) and I find that single-line AI autocomplete complements existing IDE features nicely. It works really well with the workflow I have come to prefer: describe the structure of the data using enums and structs, and then handle them using pattern matching and write unit tests for them.
muglug 2y ago
The even harder truth not mentioned here is that existing tools have a hard time understanding large codebases with well-establishd internal patterns and libraries. The article mostly talks about how AI tools can help with new things, but a large amount of software development is brownfield, not greenfield.
- greenavocado 2y ago
  This is not a problem at all as long as you use very good typing because the local contract boundaries are what matter unless you use huge amounts of global state which everybody knows is a very very bad idea and has been demonized for decades
dvas 2y ago
There are many ways of thinking and reasoning about the profession and what it means to each and every one of us. Some of the buckets: * The builders, don't care how they get the result. * The crafters, those who care how they get to the results (vim vs emacs), and folks who enjoy looking at tiny tiny details deep in the stack. * The get-it-done people, using standard IDE tools, stick with them, and it's a strict 9-5. ... And many with types, and subtypes of each ^^. In my opinion, many people have a passion for making computers to do cool things. Along the way, some of us have fallen into the mentality of using a particular tool or way of doing things and get stuck in our ways. I think it's another tool that you must know how to utilize and utilize in a way that does not atrophy your skills in the long run. I personally use it for learning and allowing me to get an in on a knowledge topic which I can then pull on and verify that the information is correct.
intended 2y ago
I found these ideas / analogies to be helpful to cut out the chatter for GenAI 1) Analogy - Using chat GPT to do code is like deciding to cross the amazon. You start moving, and half way through you realize the map is wrong. Now you are in the middle of the Amazon, without a map. 2) Reliable Matte Painting / Rough work -> I sketch, so matte painting is what GenAI reminds me of, quite a bit. It’s going to get you half way… somewhere, faster. You have to get to the end yourself. It’s easier for me to assume that GenAI is going to be mostly correct 70% of the time, and never a 100% of the time. Build and use accordingly. I’m tired about the chatter about the chatter about GenAI at this point.
deadbabe 2y ago
The big change: no more big universally used frameworks like React, Vue, etc. Every company has its own little special framework crafted by an AI with its own nuances you need to learn, and your skills will no longer transfer from company to company. Gone will be the days when you can swap out a software engineer for a similar one with the same experience in a framework you use. Every engineer coming in has to start from zero and learn exactly how to work with your special paradigms, DSLs, etc.
- aorona 2y ago
  That would be a disaster for hiring and productivity I don't any upside to creating AI generated siloes from a business or developer perspective.
  - deadbabe 2y ago
    Disaster is coming. Tragedy of the commons.
- ThrowawayR2 2y ago
  The LLM only "knows" what's in its training corpus so it's output quality is going to be comparatively crappy on private custom frameworks with only a small codebase to train on (relative to the entire corpus). Companies with private frameworks will therefore lose out. If anything, the opposite is going to happen: LLMs will cause consolidation and software evolution to grind to a halt. Coders are going to gravitate to libraries and frameworks the LLM generates the best outputs for, leaving a few big winners and the rest going extinct. New language features and new frameworks will be slow to enter general usage because of the chicken and egg problem: there will be little or code in the LLM training corpus that uses them idiomatically until some humans write enough quality code using the new features/frameworks that can be absorbed into the corpus to meaningfully affect its output.
  - deadbabe 2y ago
    Except no companies will allow substantial improvements to enter the public corpus, because they will basically be giving away their competitive advantages that will quickly be gobbled up by other LLMs. Those improvements will represent larger investments since there will be fewer engineers who can write them by hand.
agentultra 2y ago
Here are some more hard truths to add to the pile. > this kind of crawling and training is happening, regardless of whether it is ethical or not Glad we've established that it's going to change our profession regardless of ethics. > Software engineers are getting closer to finding out if AI really can make them jobless The capital class is definitely interested in this. They would love to pay fewer of us or pay us less and still get the same results. The question in 2025 might be: why would I pay you if you're not using GenAI assistants? Bob over there accepts a lower salary and puts out more code than anyone else on this team!. They may not care what the answer is: profit is all that matters. After all, they clearly don't care about the ethics of training these models, exploiting labor in countries with weak worker protections, soaking up fresh water during local droughts, etc. Why would they care about you and your work? Personally I don't find that generating code is where I do most of my programming work. I spend more of my time thinking and making sure I'm working on the right thing and that I'm building it correctly for the intended purpose. For that I want tools that aid me in my thinking: model checkers, automated theorem provers, and better type systems, etc. I need to talk to people. I don't find reviewing generated code to be especially productive even though it feels like work. I think code synthesis will be more useful. Being able to generate a working implementation from a precise specification in a higher-level language will be highly practical. There won't be a need to review the code generated once we trust the kernel since the code would be correct by construction and it can be proven how the generated code ties to the specification. We can't use GenAI to do the synthesis and replace a kernel as we still haven't solved the "black box" problem of neural nets. The problem I find with GenAI and programming is that human language is sufficiently vague for communicating with folks but too imprecise for programming. I suspect that in a few years there could be a gold mine for consulting: fixing AI-generated "house of cards" code. Hope we're all good with the coming wave of security errors, breaches, and general malfeasance that's coming with the wave of GenAI code. You think software today could be better? The current models have been trained on all the patterns that make it the way it is now. And they will generate more of it. We have to hope that "software engineers" can read enough code, fast enough, and catch those errors before they ship. Should be good times.
mooreds 2y ago
Here's a related HN discussion about the 70% article that Addy wrote on the same topic: https://news.ycombinator.com/item?id=42336553 https://news.ycombinator.com/item?id=42336553
aorona 2y ago
I have been using LLMs (chapt-gpt, perplexity, claude) for development for over a year. It is helpful for summary explanations of concepts and boilerplate for frameworks and library APIs. But it makes errors within those consistently. Its a great tool and saves a great deal of time, but I have yet to go beyond generating snippets I have to vet, typically finding a made up library API call or misunderstanding of my natural language prompt. I find it hard to pare down these LLM evangelizing articles into take aways that improve my day to day.
- nurettin 2y ago
  I know it is in the nature of probabilistic neural network outputs, but it almost feels like these commercial models are built to make those mistakes (making up functions/parameters) and it is all a big conspiracy to hide the real useful stuff from general public. I started giving the models api docs and headers before interacting with them and it seems to work a lot better.
jakozaur 2y ago
Echos with my experience. LLMs work great if you micromanage them aggressively, but the moment I put too much trust, it backfires terribly.
- verteu 2y ago
  Yeah, but that's also true for a mediocre (human) SWE... edit: My point was that, since mediocre SWEs make up a large proportion of the workforce, an LLM that performs at the level of "mediocre human" will still have massive implications for the labor force.
  - namaria 2y ago
    People keep saying that, but would you work in a team full of "mediocre" professionals or have a social circle full of "mediocre" friends if you had a choice?
  - downrightmike 2y ago
    Ideally LLMs speed them up enough that companies will use the time savings to upskill them right? Right?
Bjorkbat 2y ago
Tangentially related, I get strong Metaverse/NFT vibes around predictions on agents. Namely, a lot of predictions were made around NFTs that just didn’t make sense or were kind of dumb. My pet favorite was this notion that in the future you could bring your NFTs with you to different games and the like. You could buy a Batman NFT costume and have your guy wear it while playing metaverse World of Warcraft. They basically took Ready Player One and ran with it. Besides the fact that this is much harder to do than they could imagine, it’s also kind of a goofy idea. I feel the same way with predictions made around AI agents. My pet favorite is the notion that we stop using the internet and delegate everything to our agents. Planning a trip? Let an AI agent handle things for you. Shopping? Likewise, let an agent handle your purchases. In the future ads won’t even be targeted at people, they’ll target agents instead, and pretty soon agents won’t even browse the internet but talk to other agents instead. Is it feasible? I can’t say. I’m more interested in how goofy it all sounds. The notion that you no longer have buyer preferences while your agent gets served ads, or the notion of planning that trip to Rome or whatever and just entrusting the agent with the itinerary as if it won’t come up with unoriginal suggestions. Work agents make more sense in general, but the sentiment remains.
- coffeefirst 2y ago
  Yeah… as best I can tell, there is no such thing as an Agent. It’s a brilliant piece of marketing on top of “an automation with an LLM somewhere in the input interface.” Which is kind of neat, but it’s being sold as Jarvis and it’s more like an ATM you can talk to.
bflesch 2y ago
LLMs are an ad-free version of google search. If you use LLMs with this expectation, it'll be improvement of software engineering productivity.
- bflesch 2y ago
  AI bros downvoting this argument every time because they can't handle the truth. For people who can't install ublock origin, LLMs are a god-sent. AI bros want to keep living in their bubble where they grift millions of dollars for a better google search.
  - kbelder 2y ago
    In fairness, a better Google search is worth millions of dollars. And I agree that the primary virtue of coding with current AI is that it just cuts time that is spent looking through multiple pages of junky search results. It's not necessarily better or more accurate, but it is far faster. I'd even say that AI coding would be far less appealing if Google hadn't let its search results go to crap over the last ten years.
  - boshalfoshal 2y ago
    I mean this is clearly an incorrect take? Google search cannot reason about its contents autonomously, nor synthesize out of sample data from the things it indexes. Google search is a query over user generated content. LLMs generate content. These are fundamentally different things. Even if ChatGPT was just "lossy google" it is a much better UX than searching documents that google returns, since it can just synthesize what I want to hear in a few paragraphs, whereas with Google search I would have to know what to search and synthesize the response I'm looking for after analyzing a few things. There is an argument to be made here that we are eroding the value of intelligence and thinking for ourselves, but as a _product_ its vastly better, imo. Whether or not LLMs actually lead to AGI is yet to be seen though. But I definitely think it has reasoning capabilities that you are underating in your comparison.
  - bdangubic 2y ago
    “AI bros” have learned how to use “AI” to automate 30-40-50..% of what they used to have to do manually while others are bitching about “AI bros” on online forums. who knows, knows. who doesn’t bitches online :)
godelski 2y ago
My personal belief is just that AI assistants feel faster, not that they actually are. I'm sure they are in some specific circumstances but on average. They feel faster because your work is different, you put in different energy. I don't want to say easier (probably is) because just context switching has similar effects. People are really not reliable self evaluators. It's always the top comment on any post about some psychological study but never for these AI things. Truth is it's hard to find objective measurements. Lines of code, commits, things shipped, etc aren't strong metrics. But I think the opening of the article is important, it asks why products aren't getting better [0]. We all feel this, right? There's so much low hanging fruit that could make our lives less frustrating but is never done because it isn't flashy. Like Apple, you got all that AI but you can't use a regex to merge calendars? Google, you can't allow a test task to be made in the past (extra helpful when it repeats). Wikipedia still uses the m address and you go to the mobile site from desktop if you don't manually remove? I could go on and on but I think we're just in the wrong headspace. [0] imo products are getting worse, but that decline started before GPT
- MrMcCall 2y ago
  > imo products are getting worse, but that decline started before GPT Yes, and it's not going to change until the motivations and methodologies that drive the corporation change. All these LLMs are just reinforcing their MBA mentality, which is to try to do more with less, which just ends up being more enshittification. The money people only care about one thing, and one thing only. I say that the only real qualification for being a manager is to be willing to prioritize money over people. I've had exactly one good manager in my career, because he knew his role and respected that I knew and was passionate about mine while respecting his. A main problem with all these huge software companies is that they are too busy chasing new features while letting long-existing bugs remain the bane of the users' experience. There's no "lets fix the current issues before we embark on new projects" mentality. I literally encountered this exact mentality in a dumb little marketing company 25yo. Different time, very small company, same dumbasses running the show. When large software tries to be adapted beyond its initial design specs, it will eventually collapse under its own weight. At some point, a fundamental redesign must occur to prevent change grinding to a halt.
siliconc0w 2y ago
You can kinda see an agent that automates a 'best practices' AI-assisted workflow to iterates with AI to generate and run tests, optimize code, and feed in the right examples or signatures so it can generate code that properly uses existing APIs. Maybe trying to use cheaper models first and then calling the more expensive models to iterate and get through tests or errors. I haven't really seen anything like this so I imagine it's a lot harder than I'm imagining.
sega_sai 2y ago
It is a somewhat interesting take. What I am interested in as a person teaching a computing course, what is the best way to force people to understand/interact with the code coming from the LLM. I.e. when I give computing problems to students, it is often easy to put the problem in chatgpt and get an answer. In a very significant fraction of cases the code would be somewhat sensible and would pass the tests. In some cases the output would use the wrong approach or would fail the test, but not often enough to completely discourage cheating. In the end this comes down to the question of what skills we want from people writing code with the help of the LLM, and how to test for those skills. (here I'm not talking about professional programmers, but scientists rather)
localghost3000 2y ago
LLM’s have replaced Stack Overflow for me. Occasionally I can use them to write a simple bash script for me or some bit of terraform that I don’t feel like looking up. Useful but not exactly life changing. What I would consider a game changer would be generating USEFUL unit and integration tests. Ideally that used the existing fixtures and utilities already in place. I’ve yet to see that happen even with code the LLM had just generated.
- MrMcCall 2y ago
  "Predicting the next word" is never going to facilitate modifying complex code in any way that won't be a disaster. Creating sensible, useful, comprehensive tests requires a comprehensive understanding of the code -- there is no shortcut for any but the simplest of algorithms. "Short cuts make long delays." --Tolkien
  - localghost3000 2y ago
    Just so. That’s kind of my point. In order for them to be the revolution the tech industry wants us to believe they are, they need to be able to handle necessary but laborious tasks like tests for us. I don’t think it’s possible with the current approaches however
LeicaLatte 2y ago
Flow state code != production quality code. Not yet. From personal experience, writing real-life production code is like running a marathon; it requires endurance and rigor. I've seen AI-generated code — it’s more like a treadmill run, fine for practice only. Unpredictable issues, hallucinations pop up all the time with AI code, I have to rely on my own skills to navigate and solve problems.
deleted 2y ago
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uludag 2y ago
I was reading the book "How Big Things Get Done," which is immensely applicable to the field of software engineering. The book is about how and why big projects fail. The book mentioned that IT projects were among the worst in cost and time overrun. I see essentially a win-win situation for software developers: Either, AI will enhance the work of software engineering on a fundamental level, helping SWE projects to be delivered (more) on time and with high(er) quality (I can't state how amazing this would be) OR things won't get significantly better, projects still can't reliably be delivered, software quality doesn't get better, etc. (the robots won't be taking our jobs) It will be interesting to see which future we'll end up.
rstuart4133 2y ago
> It's becoming a pattern: Teams use AI to rapidly build impressive demos. The happy path works beautifully. Investors and social networks are wowed. But when real users start clicking around? That's when things fall apart. Oh no. We will have to endure another sort of AI slop infesting the web? It's bad enough as it is. Most smaller web sites are already broken in tiny ways. Who hasn't had to break out the browser debugger just get past some web sites broken order page? Sloppy reviews, images, chat bots, and phishing are everywhere now. In this brave new world someone no computer experience tinkering a home with can produced a beautiful looking web site that's broken it 1000's of ways, we are going to be overrun with this crap. And they are going to be harvesting login email addresses and passwords. It's going to be a rough decade.
- raxxor 2y ago
  > Last year, we saw that about 75% of developers use some kind of AI tool for software engineering I heavily doubt that if I look left and right. They are prompting them in a browser, but few have adopted AI in their toolchain yet. > “Software engineers are getting closer to finding out if AI really can make them jobless” It might indeed partially work if people are able to describe a problem and prompt an AI. Do they know how to describe a problem and prompt an AI, rhetorical question? In practice is does indeed allow other engineers not in software to churn out some scripts and they mostly are of good or at least decent quality. But for complex work or integration, the AIs just lack any context that is needed for the specific problem to be solved. Still, AIs do help the developers themselves.
mtrovo 2y ago
This is an amazing breakdown. I can't believe how quickly AI tools have become integral to our workflows. Completely agree with the idea that experienced developers will be even more valuable in the future.
m463 2y ago
I think this might be like those static analysis tools. people run their code through it, and it finds a LOT of problems, some of them serious. and then you fix them and you feel good. But after that, you have a bunch of problems that aren't real. You either ignore them or the tool starts creating more work exponentially. I suspect AI will be like that. It will help you a bit, but don't get caught up in it because you'll spend time distracted doing AI things.
polishdude20 2y ago
I've tried using Claude for the first time to help me write some wifi code for an ESP32. It gets like 90% of the way there and I can't get the last 10% to where it works how I need it. Every time I mention an issue with the code, it rewrites it and a new issue pops up. Then I me tiin that and it rewrites it with the original issue again. It's like it forgets the past mistakes it's made. Also there's been multiple times where it just forgets a closing parenthesis in a function or tells me a function definition doesn't exist in the code even though it's literally right there.
bionhoward 2y ago
the biggest reason not to use LLMs is the customer noncompete clauses where they’re learning from the conversations but you agree not to “develop models that compete” — makes me think everyone using OpenAI, Anthropic, Gemini API, Mistral, Copilot, Perplexity, etc is dumb or addicted to short term benefits while totally ignoring the long term consequences of paying to train your own replacement while agreeing not to compete
- bdangubic 2y ago
  smart people run LLM locally :)
lincpa 2y ago
[dead]
monkeydust 2y ago
The unintended consequence of this is well captured in Bainbridge's piece from 1983 'Ironies of Automation' https://www.sciencedirect.com/science/article/abs/pii/0005109883900468 https://www.sciencedirect.com/science/article/abs/pii/000510... - The more tasks you automate the less practice people get in doing those tasks themselves and developing the experience in executing them. - Yet, experience becomes more important as issues/exceptions occur (which they will) - Ironically, when people are needed the most they are least prepared to step in because automation has taken over their day-to-day. Net result might be a reduced supply of 'experience' but demand remains strong thus increasing the price of it.
leovingi 2y ago
> The irony? AI tools might actually enable this renaissance. By handling the routine coding tasks, they free up developers to focus on what matters most: creating software that truly serves and delights users. How very naive. All productivity and efficiency gains will be utilized to push out an ever-increasing stream of new features because that is what drives sales and that is what the business needs. It is the exact same reason why widening a highway does not actually reduce traffic congestion.

91 points by pseudolus 2y ago

deleted 2y ago

[deleted]

The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. Having said that the article seems to accurately reflect what it's like using the current tools. But how could anyone reasonably expect the situation to be similar 3-5 years down the line? If they just didn't frame it as a prediction then it would make sense.

rileymat2 2y ago
> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? We can’t, that’s why we are talking about the current capabilities. I remember using Dragon Natural Dictation software before the year 2000, I was young and it blew my mind away with the possibilities. 27 years later it still has not lived up to my young imagination. (It has gotten a ton better, no doubt, and will continue to with more AI)
- tessierashpool 2y ago
  spoiler alert: this always happens with new technologies, to some extent. the possibilities that materialize are always an odd subset of the possibilities that people envision when the tech is new. you can get better at predicting that subset by studying economics or the humanities, but randomness also plays a crucial role.
  - mooreds 2y ago
    Or it could be because low hanging fruit is low hanging.
- ilaksh 2y ago
  AssemblyAI's most recent models are incredible. I think you aren't accurately assessing leading edge STT or how your young self would judge this capability level.
CharlesW 2y ago
> The problem with this is that it's supposedly about predicting the future but actually bases everything on LLM's current capabilities. Is it possible you missed some of it? The entire last half is dedicated to predicting the future based on an agentic future that's (as the author notes) "a big unknown" as we have this conversation.
- ilaksh 2y ago
  It's not the entire last half. He does mention agents, but inaccurately says that Devin is the only agentic software engineering tool. But he does not override his initial premise of doubting that things will change significantly from the current uses in the future. It's not really unknown, there are several popular tools, and we can expect them to be more reliable and useful as better models continue to be rolled out. Also if you are writing about the future then you should look beyond next year. Everyone should anticipate that there is a strong possibility that we continue on a similar trajectory of improvement. Therefore it is NOT reasonable to expect that things won't change dramatically in software engineering, because the trajectory is very rapid progress. Within the next 3, 5, maybe 10 years, many existing jobs including software engineering may be replaced by AI. That's the direction we are headed and any article should take the possibility seriously by now.
swiftcoder 2y ago
> But how could anyone reasonably expect the situation to be similar 3-5 years down the line? That just depends how optimistic you are about rate of improvement. Folks who think we are on the cusp of AGI predict that improvement will be exponential. Folks who think that Moores law will keep up with training costs predict improvement that is somewhat linear. On the other hand, folks who are looking at the rate of change of current LLMs think we're running out of training data, predict that improvements are more likely to be logarithmic, and we're already in the flattening section of the curve...
- ilaksh 2y ago
  There are many curves. It's a series of sigmoids. There are many innovations to keep up with the expected constant performance increases and demand. They started running out of data, then curated it, then spent more time on inference. There are many small and large innovations that continue to improve performance as each improvement gets implemented and maximized and the gains flatten out and then increase again. Some are using giant SRAM chips. They will then connect them with optics. When that doesn't keep up with demand, probably memristors or something will be scaled to be the next paradigm. Look at the massive improvements made by the DeepSeek team to the open source SOTA recently.
- godelski 2y ago
  I believe growth is exponential. The growth of complexity. The author mentions how it can get you 70% of the way there, well Pareto is a bitch. Once the details matter more the difficulty grows evidentially (well power series). What people fail to realize is despite what we've done is incredibly and no easy task, it's "the easy part". And progress is unfortunately constantly like this. Because as we progress we are effectively solving higher order solutions. Solve your Taylor series problems. If you've done up to 4th order and still need 5th, everything previously is (usually) "easy" in comparison. Getting to the 6th is no different, and so on. And of course it gets harder, we made progress! The naive part is to think the future will be as easy as the past looks in retrospect, not as hard as the current state looked a priori.
  - intended 2y ago
logicchains 2y ago
>It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve Because it's incredibly difficult for people to accept that the career they put years into and love might not exist in 5-10 years. At the current pace, in a few years we'll have something smarter than o3 while no more expensive than o1. Then all it would take is someone to find a nice way to rig it up with short-term memory and wrap it in something like Devin, and then companies would be able to hire the equivalent of a top 1% remote dev for less than 10% of a current dev salary.
intended 2y ago
To be fair - the gap between present capabilities and future capabilities is the issue. Having a system that is accurate a random number of times, is very different than a system that is predictable. There’s just too many use cases where GenAI looks like a great fit, but then you have to have the mental overhead of figuring out if this is the time you lose the lottery. It’s just… the kind of mental overhead I wanted a machine to take away from me, not add to my life.
PaulDavisThe1st 2y ago
> It's incredible that people still haven't figured out or won't accept or plan for technology to continue to improve. Especially given how obvious the rapid improvement in this area has been. If you wrote this about aeroplanes in the mid-1960s, despite the previous 40-50 years of rapid improvement, you'd be wrong. The same would be true for bicycles, and automobiles - despite decades of incremental and noteworthy-for-specific-context improvements, there's been no fundamental changes in these (and many other technologies) that reflects the early progress. Yes, modern cars are safer, more comfortable, more fuel efficient (sometimes), but they get nothing done that wasn't possible with a car from the mid-1950s. Why would you assume that the recent past of LLMs provides an outline of what the future of AI in general (not just LLMs) is going to be?
- dwaltrip 2y ago
  The question is, are current LLMs more like wooden biplanes or early passenger jets? It feels like we are closet to the wooden biplane era, imo.
  - PaulDavisThe1st 2y ago
    Or are they more like a video of flying that we're watching and saying with glee "we're flying!" ?
DanHulton 2y ago
The situation certainly won't be similar 3-5 years down the line, but it also won't necessarily be different in the way that people are predicting. In the section about the 70% problem, the author writes: > The good news? This gap will likely narrow as tools improve. This is not a fact, it's a prediction, an article of faith. It's a common prediction, that these tools will only get better over time, but that's not guaranteed! I think it's likely as well, provided you define "improve" incredibly pedantically, but the unspoken part of this prediction is that the tools will improve _significantly,_ and _that_ part is one I have doubts about. Honestly, I think it's pretty likely that we've just about hit the local maximum of our current techniques - training newer, bigger models is fantastically expensive and doesn't seem to have the same jumps in capability as earlier generations did, and stringing together a bunch of models in an agent produces frankly only modest improvements for the cost increase. I've written about "the 70% problem" before (not by name, though), and my biggest worry there is that you require experience and good judgement to be able to use these tools effectively, and these tools by their nature erode that good judgement and deny you the experience. Juniors won't have to work through "the tough parts" of programming and build up the skills required to understand when an LLM is leading them astray, and even experienced programmers can lose familiarity with their own codebases as they rely on LLMs to provide the "understanding." Think of all the times where you haven't had to interact with a service for months, how long it takes to warm back up to it -- what about when that's _most_ of your code, because LLMs have been preventing you from needing to gain any deep understanding about it? What happens if, in 3-5 years, LLMs don't get significantly better, but we've stopped producing properly capable Intermediate and Senior developers, and even started atrophying the skills of existing Seniors? (Couple this with LLM-using developers who can move mountains in a day to kickstart new projects and get promoted, but leave behind a trashfire of a codebase for more "classically" experienced developers to clean up and maintain at a much slower pace. That's a problem that's _always_ been in the industry, just magnified due to the power of LLMs.)
- MrMcCall 2y ago
  [flagged]
  - 2y ago

gazchop 2y ago

Different take: AI writes a lot of mediocre code for us so we don't have to and we're impressed at that. But that's not the problem we need to solve. All our programming languages are verbose and stupid. It takes too much effort to solve the problems we do in them.

asdff 2y ago
You’d think ai models would be good enough to write code in pure binary. Python is for humans. Take the human out there’s no point in it.
- gazchop 2y ago
  You think something which gets 70% of stuff right on a very good day when the planets are all aligned can write assembly that works? I have news for you.
  - asdff 2y ago
    I think it sure can. Randomly modify a binary function over a million potential permutations and measure the distribution of output, subset from the distribution the binary function(s) that output ahead of your threshold. Should give you a fast function that does what you want with absolutely indescribable code, which isn’t an issue if it does what you want with reasonable performance.
- koe123 2y ago
  I suppose the issue is a data problem, there being relatively little high quality data explaining how things should be solved in binary. As such making the learning mapping between prompt (english) and good solution (binary) difficult.
  - ninetyninenine 2y ago
    The data is easily generated by compiling the code.

ilrwbwrkhv 2y ago

I heavily started using windscribe and cursor editor. Mind you, I have 10 years of experience. But lately I have cut down their usage and gone back to writing stuff by hand. Because it is so easy to apply the changes the AI suggests but there's this subtle shift over time in the code base towards a non-optimal architecture. And it becomes really hard to get out of. The place where I still use AI quite a lot is autocomplete but of the intelligent kind like if I am returning a different string for each enum, all of that gets autocompleted really fast. So line completion models like what JetBrains provides for free is I think the right balance. Supermaven also works well.

8s2ngy 2y ago
I am in the same boat with you. I have also come to dislike having AI code generator inside my editor. Recently, I started looking into learning Rust seriously and decided to go with RustRover (the rust IDE from JetBrains) and I find that single-line AI autocomplete complements existing IDE features nicely. It works really well with the workflow I have come to prefer: describe the structure of the data using enums and structs, and then handle them using pattern matching and write unit tests for them.

muglug 2y ago

The even harder truth not mentioned here is that existing tools have a hard time understanding large codebases with well-establishd internal patterns and libraries. The article mostly talks about how AI tools can help with new things, but a large amount of software development is brownfield, not greenfield.

greenavocado 2y ago
This is not a problem at all as long as you use very good typing because the local contract boundaries are what matter unless you use huge amounts of global state which everybody knows is a very very bad idea and has been demonized for decades

dvas 2y ago

There are many ways of thinking and reasoning about the profession and what it means to each and every one of us. Some of the buckets: * The builders, don't care how they get the result. * The crafters, those who care how they get to the results (vim vs emacs), and folks who enjoy looking at tiny tiny details deep in the stack. * The get-it-done people, using standard IDE tools, stick with them, and it's a strict 9-5. ... And many with types, and subtypes of each ^^. In my opinion, many people have a passion for making computers to do cool things. Along the way, some of us have fallen into the mentality of using a particular tool or way of doing things and get stuck in our ways. I think it's another tool that you must know how to utilize and utilize in a way that does not atrophy your skills in the long run. I personally use it for learning and allowing me to get an in on a knowledge topic which I can then pull on and verify that the information is correct.

intended 2y ago

I found these ideas / analogies to be helpful to cut out the chatter for GenAI 1) Analogy - Using chat GPT to do code is like deciding to cross the amazon. You start moving, and half way through you realize the map is wrong. Now you are in the middle of the Amazon, without a map. 2) Reliable Matte Painting / Rough work -> I sketch, so matte painting is what GenAI reminds me of, quite a bit. It’s going to get you half way… somewhere, faster. You have to get to the end yourself. It’s easier for me to assume that GenAI is going to be mostly correct 70% of the time, and never a 100% of the time. Build and use accordingly. I’m tired about the chatter about the chatter about GenAI at this point.

deadbabe 2y ago

The big change: no more big universally used frameworks like React, Vue, etc. Every company has its own little special framework crafted by an AI with its own nuances you need to learn, and your skills will no longer transfer from company to company. Gone will be the days when you can swap out a software engineer for a similar one with the same experience in a framework you use. Every engineer coming in has to start from zero and learn exactly how to work with your special paradigms, DSLs, etc.

aorona 2y ago
That would be a disaster for hiring and productivity I don't any upside to creating AI generated siloes from a business or developer perspective.
- deadbabe 2y ago
  Disaster is coming. Tragedy of the commons.
ThrowawayR2 2y ago
The LLM only "knows" what's in its training corpus so it's output quality is going to be comparatively crappy on private custom frameworks with only a small codebase to train on (relative to the entire corpus). Companies with private frameworks will therefore lose out. If anything, the opposite is going to happen: LLMs will cause consolidation and software evolution to grind to a halt. Coders are going to gravitate to libraries and frameworks the LLM generates the best outputs for, leaving a few big winners and the rest going extinct. New language features and new frameworks will be slow to enter general usage because of the chicken and egg problem: there will be little or code in the LLM training corpus that uses them idiomatically until some humans write enough quality code using the new features/frameworks that can be absorbed into the corpus to meaningfully affect its output.
- deadbabe 2y ago
  Except no companies will allow substantial improvements to enter the public corpus, because they will basically be giving away their competitive advantages that will quickly be gobbled up by other LLMs. Those improvements will represent larger investments since there will be fewer engineers who can write them by hand.

agentultra 2y ago

Here are some more hard truths to add to the pile. > this kind of crawling and training is happening, regardless of whether it is ethical or not Glad we've established that it's going to change our profession regardless of ethics. > Software engineers are getting closer to finding out if AI really can make them jobless The capital class is definitely interested in this. They would love to pay fewer of us or pay us less and still get the same results. The question in 2025 might be: why would I pay you if you're not using GenAI assistants? Bob over there accepts a lower salary and puts out more code than anyone else on this team!. They may not care what the answer is: profit is all that matters. After all, they clearly don't care about the ethics of training these models, exploiting labor in countries with weak worker protections, soaking up fresh water during local droughts, etc. Why would they care about you and your work? Personally I don't find that generating code is where I do most of my programming work. I spend more of my time thinking and making sure I'm working on the right thing and that I'm building it correctly for the intended purpose. For that I want tools that aid me in my thinking: model checkers, automated theorem provers, and better type systems, etc. I need to talk to people. I don't find reviewing generated code to be especially productive even though it feels like work. I think code synthesis will be more useful. Being able to generate a working implementation from a precise specification in a higher-level language will be highly practical. There won't be a need to review the code generated once we trust the kernel since the code would be correct by construction and it can be proven how the generated code ties to the specification. We can't use GenAI to do the synthesis and replace a kernel as we still haven't solved the "black box" problem of neural nets. The problem I find with GenAI and programming is that human language is sufficiently vague for communicating with folks but too imprecise for programming. I suspect that in a few years there could be a gold mine for consulting: fixing AI-generated "house of cards" code. Hope we're all good with the coming wave of security errors, breaches, and general malfeasance that's coming with the wave of GenAI code. You think software today could be better? The current models have been trained on all the patterns that make it the way it is now. And they will generate more of it. We have to hope that "software engineers" can read enough code, fast enough, and catch those errors before they ship. Should be good times.

mooreds 2y ago

Here's a related HN discussion about the 70% article that Addy wrote on the same topic: https://news.ycombinator.com/item?id=42336553 https://news.ycombinator.com/item?id=42336553

aorona 2y ago

I have been using LLMs (chapt-gpt, perplexity, claude) for development for over a year. It is helpful for summary explanations of concepts and boilerplate for frameworks and library APIs. But it makes errors within those consistently. Its a great tool and saves a great deal of time, but I have yet to go beyond generating snippets I have to vet, typically finding a made up library API call or misunderstanding of my natural language prompt. I find it hard to pare down these LLM evangelizing articles into take aways that improve my day to day.

nurettin 2y ago
I know it is in the nature of probabilistic neural network outputs, but it almost feels like these commercial models are built to make those mistakes (making up functions/parameters) and it is all a big conspiracy to hide the real useful stuff from general public. I started giving the models api docs and headers before interacting with them and it seems to work a lot better.

jakozaur 2y ago

Echos with my experience. LLMs work great if you micromanage them aggressively, but the moment I put too much trust, it backfires terribly.

verteu 2y ago
Yeah, but that's also true for a mediocre (human) SWE... edit: My point was that, since mediocre SWEs make up a large proportion of the workforce, an LLM that performs at the level of "mediocre human" will still have massive implications for the labor force.
- namaria 2y ago
  People keep saying that, but would you work in a team full of "mediocre" professionals or have a social circle full of "mediocre" friends if you had a choice?
- downrightmike 2y ago
  Ideally LLMs speed them up enough that companies will use the time savings to upskill them right? Right?

Bjorkbat 2y ago

Tangentially related, I get strong Metaverse/NFT vibes around predictions on agents. Namely, a lot of predictions were made around NFTs that just didn’t make sense or were kind of dumb. My pet favorite was this notion that in the future you could bring your NFTs with you to different games and the like. You could buy a Batman NFT costume and have your guy wear it while playing metaverse World of Warcraft. They basically took Ready Player One and ran with it. Besides the fact that this is much harder to do than they could imagine, it’s also kind of a goofy idea. I feel the same way with predictions made around AI agents. My pet favorite is the notion that we stop using the internet and delegate everything to our agents. Planning a trip? Let an AI agent handle things for you. Shopping? Likewise, let an agent handle your purchases. In the future ads won’t even be targeted at people, they’ll target agents instead, and pretty soon agents won’t even browse the internet but talk to other agents instead. Is it feasible? I can’t say. I’m more interested in how goofy it all sounds. The notion that you no longer have buyer preferences while your agent gets served ads, or the notion of planning that trip to Rome or whatever and just entrusting the agent with the itinerary as if it won’t come up with unoriginal suggestions. Work agents make more sense in general, but the sentiment remains.

coffeefirst 2y ago
Yeah… as best I can tell, there is no such thing as an Agent. It’s a brilliant piece of marketing on top of “an automation with an LLM somewhere in the input interface.” Which is kind of neat, but it’s being sold as Jarvis and it’s more like an ATM you can talk to.

bflesch 2y ago

LLMs are an ad-free version of google search. If you use LLMs with this expectation, it'll be improvement of software engineering productivity.

bflesch 2y ago
AI bros downvoting this argument every time because they can't handle the truth. For people who can't install ublock origin, LLMs are a god-sent. AI bros want to keep living in their bubble where they grift millions of dollars for a better google search.
- kbelder 2y ago
  In fairness, a better Google search is worth millions of dollars. And I agree that the primary virtue of coding with current AI is that it just cuts time that is spent looking through multiple pages of junky search results. It's not necessarily better or more accurate, but it is far faster. I'd even say that AI coding would be far less appealing if Google hadn't let its search results go to crap over the last ten years.
- boshalfoshal 2y ago
  I mean this is clearly an incorrect take? Google search cannot reason about its contents autonomously, nor synthesize out of sample data from the things it indexes. Google search is a query over user generated content. LLMs generate content. These are fundamentally different things. Even if ChatGPT was just "lossy google" it is a much better UX than searching documents that google returns, since it can just synthesize what I want to hear in a few paragraphs, whereas with Google search I would have to know what to search and synthesize the response I'm looking for after analyzing a few things. There is an argument to be made here that we are eroding the value of intelligence and thinking for ourselves, but as a _product_ its vastly better, imo. Whether or not LLMs actually lead to AGI is yet to be seen though. But I definitely think it has reasoning capabilities that you are underating in your comparison.
- bdangubic 2y ago
  “AI bros” have learned how to use “AI” to automate 30-40-50..% of what they used to have to do manually while others are bitching about “AI bros” on online forums. who knows, knows. who doesn’t bitches online :)

godelski 2y ago

My personal belief is just that AI assistants feel faster, not that they actually are. I'm sure they are in some specific circumstances but on average. They feel faster because your work is different, you put in different energy. I don't want to say easier (probably is) because just context switching has similar effects. People are really not reliable self evaluators. It's always the top comment on any post about some psychological study but never for these AI things. Truth is it's hard to find objective measurements. Lines of code, commits, things shipped, etc aren't strong metrics. But I think the opening of the article is important, it asks why products aren't getting better [0]. We all feel this, right? There's so much low hanging fruit that could make our lives less frustrating but is never done because it isn't flashy. Like Apple, you got all that AI but you can't use a regex to merge calendars? Google, you can't allow a test task to be made in the past (extra helpful when it repeats). Wikipedia still uses the m address and you go to the mobile site from desktop if you don't manually remove? I could go on and on but I think we're just in the wrong headspace. [0] imo products are getting worse, but that decline started before GPT

MrMcCall 2y ago
> imo products are getting worse, but that decline started before GPT Yes, and it's not going to change until the motivations and methodologies that drive the corporation change. All these LLMs are just reinforcing their MBA mentality, which is to try to do more with less, which just ends up being more enshittification. The money people only care about one thing, and one thing only. I say that the only real qualification for being a manager is to be willing to prioritize money over people. I've had exactly one good manager in my career, because he knew his role and respected that I knew and was passionate about mine while respecting his. A main problem with all these huge software companies is that they are too busy chasing new features while letting long-existing bugs remain the bane of the users' experience. There's no "lets fix the current issues before we embark on new projects" mentality. I literally encountered this exact mentality in a dumb little marketing company 25yo. Different time, very small company, same dumbasses running the show. When large software tries to be adapted beyond its initial design specs, it will eventually collapse under its own weight. At some point, a fundamental redesign must occur to prevent change grinding to a halt.

siliconc0w 2y ago

You can kinda see an agent that automates a 'best practices' AI-assisted workflow to iterates with AI to generate and run tests, optimize code, and feed in the right examples or signatures so it can generate code that properly uses existing APIs. Maybe trying to use cheaper models first and then calling the more expensive models to iterate and get through tests or errors. I haven't really seen anything like this so I imagine it's a lot harder than I'm imagining.

sega_sai 2y ago

It is a somewhat interesting take. What I am interested in as a person teaching a computing course, what is the best way to force people to understand/interact with the code coming from the LLM. I.e. when I give computing problems to students, it is often easy to put the problem in chatgpt and get an answer. In a very significant fraction of cases the code would be somewhat sensible and would pass the tests. In some cases the output would use the wrong approach or would fail the test, but not often enough to completely discourage cheating. In the end this comes down to the question of what skills we want from people writing code with the help of the LLM, and how to test for those skills. (here I'm not talking about professional programmers, but scientists rather)

localghost3000 2y ago

LLM’s have replaced Stack Overflow for me. Occasionally I can use them to write a simple bash script for me or some bit of terraform that I don’t feel like looking up. Useful but not exactly life changing. What I would consider a game changer would be generating USEFUL unit and integration tests. Ideally that used the existing fixtures and utilities already in place. I’ve yet to see that happen even with code the LLM had just generated.

MrMcCall 2y ago
"Predicting the next word" is never going to facilitate modifying complex code in any way that won't be a disaster. Creating sensible, useful, comprehensive tests requires a comprehensive understanding of the code -- there is no shortcut for any but the simplest of algorithms. "Short cuts make long delays." --Tolkien
- localghost3000 2y ago
  Just so. That’s kind of my point. In order for them to be the revolution the tech industry wants us to believe they are, they need to be able to handle necessary but laborious tasks like tests for us. I don’t think it’s possible with the current approaches however

LeicaLatte 2y ago

Flow state code != production quality code. Not yet. From personal experience, writing real-life production code is like running a marathon; it requires endurance and rigor. I've seen AI-generated code — it’s more like a treadmill run, fine for practice only. Unpredictable issues, hallucinations pop up all the time with AI code, I have to rely on my own skills to navigate and solve problems.

deleted 2y ago

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uludag 2y ago

I was reading the book "How Big Things Get Done," which is immensely applicable to the field of software engineering. The book is about how and why big projects fail. The book mentioned that IT projects were among the worst in cost and time overrun. I see essentially a win-win situation for software developers: Either, AI will enhance the work of software engineering on a fundamental level, helping SWE projects to be delivered (more) on time and with high(er) quality (I can't state how amazing this would be) OR things won't get significantly better, projects still can't reliably be delivered, software quality doesn't get better, etc. (the robots won't be taking our jobs) It will be interesting to see which future we'll end up.

rstuart4133 2y ago

> It's becoming a pattern: Teams use AI to rapidly build impressive demos. The happy path works beautifully. Investors and social networks are wowed. But when real users start clicking around? That's when things fall apart. Oh no. We will have to endure another sort of AI slop infesting the web? It's bad enough as it is. Most smaller web sites are already broken in tiny ways. Who hasn't had to break out the browser debugger just get past some web sites broken order page? Sloppy reviews, images, chat bots, and phishing are everywhere now. In this brave new world someone no computer experience tinkering a home with can produced a beautiful looking web site that's broken it 1000's of ways, we are going to be overrun with this crap. And they are going to be harvesting login email addresses and passwords. It's going to be a rough decade.

raxxor 2y ago
> Last year, we saw that about 75% of developers use some kind of AI tool for software engineering I heavily doubt that if I look left and right. They are prompting them in a browser, but few have adopted AI in their toolchain yet. > “Software engineers are getting closer to finding out if AI really can make them jobless” It might indeed partially work if people are able to describe a problem and prompt an AI. Do they know how to describe a problem and prompt an AI, rhetorical question? In practice is does indeed allow other engineers not in software to churn out some scripts and they mostly are of good or at least decent quality. But for complex work or integration, the AIs just lack any context that is needed for the specific problem to be solved. Still, AIs do help the developers themselves.

mtrovo 2y ago

This is an amazing breakdown. I can't believe how quickly AI tools have become integral to our workflows. Completely agree with the idea that experienced developers will be even more valuable in the future.

m463 2y ago

I think this might be like those static analysis tools. people run their code through it, and it finds a LOT of problems, some of them serious. and then you fix them and you feel good. But after that, you have a bunch of problems that aren't real. You either ignore them or the tool starts creating more work exponentially. I suspect AI will be like that. It will help you a bit, but don't get caught up in it because you'll spend time distracted doing AI things.

polishdude20 2y ago

I've tried using Claude for the first time to help me write some wifi code for an ESP32. It gets like 90% of the way there and I can't get the last 10% to where it works how I need it. Every time I mention an issue with the code, it rewrites it and a new issue pops up. Then I me tiin that and it rewrites it with the original issue again. It's like it forgets the past mistakes it's made. Also there's been multiple times where it just forgets a closing parenthesis in a function or tells me a function definition doesn't exist in the code even though it's literally right there.

bionhoward 2y ago

the biggest reason not to use LLMs is the customer noncompete clauses where they’re learning from the conversations but you agree not to “develop models that compete” — makes me think everyone using OpenAI, Anthropic, Gemini API, Mistral, Copilot, Perplexity, etc is dumb or addicted to short term benefits while totally ignoring the long term consequences of paying to train your own replacement while agreeing not to compete

bdangubic 2y ago
smart people run LLM locally :)

lincpa 2y ago

[dead]

monkeydust 2y ago

The unintended consequence of this is well captured in Bainbridge's piece from 1983 'Ironies of Automation' https://www.sciencedirect.com/science/article/abs/pii/0005109883900468 https://www.sciencedirect.com/science/article/abs/pii/000510... - The more tasks you automate the less practice people get in doing those tasks themselves and developing the experience in executing them. - Yet, experience becomes more important as issues/exceptions occur (which they will) - Ironically, when people are needed the most they are least prepared to step in because automation has taken over their day-to-day. Net result might be a reduced supply of 'experience' but demand remains strong thus increasing the price of it.

leovingi 2y ago

> The irony? AI tools might actually enable this renaissance. By handling the routine coding tasks, they free up developers to focus on what matters most: creating software that truly serves and delights users. How very naive. All productivity and efficiency gains will be utilized to push out an ever-increasing stream of new features because that is what drives sales and that is what the business needs. It is the exact same reason why widening a highway does not actually reduce traffic congestion.