This is ominous and very depressing given what we've recently learned / reconfirmed about LLMs sapping our ability to persist through difficult problems:
> There were 2 or 3 bugs that stumped me, and after 20 min or so of debugging I asked Claude for some advice. But most of the debugging was by hand!
Twenty whole minutes. Us old-timers (I am 39) are chortling.
I am not trying to knock the author specifically. But he was doing this for education, not for work. He should have spent more like 6 hours before desperately reaching for the LLM. I imagine after 1 hour he would have figured it out on his own.
Why shouldn't someone consult some kind of external resource for help, after struggling with a specific coding problem for 20 minutes? Why is 6 hours the right amount of time to timebox this to?
You say this as if the LLM isn’t committing things it doesn’t even recognize as bugs if you don’t babysit it. I’d rather have a codebase with a few very well marked evil zones, rather than a codebase no one has read. All code contains demons and it’s good to have an understanding of their locations and relative power
I’ve worked in financial modelling before where you need to make sure results are correct, not approximate.
One time there was a nasty bug in pandas multiindexes (admittedly we banned pandas for all new code because it just can’t do semver).
Spent 9 days to debug three lines of code.
Endurance and patience are learned skills and sometimes they’re the only way you can get a correct verifiable solution.
Why? I’m as old timer as old timer can get - started programming as a hobby in 1986 in assembly on an Apple //e in 65C02 assembly language.
But just today a bug was reported by a customer (we are still in testing not a production bug). I implemented this project myself from an empty git repo and an empty AWS account including 3 weeks of pre implementation discovery.
I reproduced the issue and through the problem at Claude with nothing but two pieces of information - the ID of the event showing the bug and the description.
It worked backwards looking at the event stream in the database, looking at the code that stored the event stream, looking at the code that generated the event stream (separate Lambda), looking at the actual config table and found the root cause in 3 minutes.
After looking at the code locally, it even looked at the cached artifacts of my build and verified that what was deployed was the same thing that I had locally (same lambda deployment version in AWS as my artifacts). I had it document the debug steps it took in an md file.
Why make life harder on myself? Even if it were something I was doing as a hobby, I have a wife who I want to spend time with, I’m a gym rat and I’m learning Spanish. Why would I waste 6 hours doing something that a computer could do for me in 5 minutes?
Assuming he has a day job and gets off at 6, he would be spending all of his off time chasing down a bug that he could be using doing something else.
Often when LLMs give me some command option or advice I haven’t seen before I try to independently verify it. And I’ve often been frustrated just how hard it is to find this info from the source documents.
Though a lot of the time this is more an inefficiency of the documentation and Google rather than something only LLMs could do.
As the rate of 'hallucinations' seems to have dropped dramatically (at least IME as regards non-existent flags and the like), I'm more concerned with usage. I often use grep.app/GH code search to look for usage examples as a sanity check when things look "off", for exactly the reason you described--there's often a total lack of good documentation on things like that, especially on "younger" tools/stuff.
I'm sure the author will encounter problems where the only way to solve them will be the marginal effort provided by a human. At that point he won't be just be solving problems to work his brain, but also to accomplish a goal.
YES. I don't know how many multi WEEK sessions of debugging I've been through in my career. Frustrating, but so many valuable lessons learned in the process. LLMs are absolutely causing us to lose something very important.
Having a tool that instantly searches through the first 50 pages of google and comes up with a reasonable solution is just speeding up what I would have done manually anyways.
Would I have learned more about (and around) the system I‘m building? Absolutely. I just prefer making my system work over anything else, so I don’t mind losing that.
The multi week debugging sessions weren't fun, but that doesn't mean they weren't valuable and important and a growth and learning opportunity that we now will no longer experience.
Seems like there's a good argument to be made that we'll have plenty of opportunities for valuable growth and learning, just about different things. Just like it's always been with technology. The machine does some of the stuff I used to do so now I do some different stuff.
IMO the more salient point is that bugs requiring multiple weeks of human work aren't going away! Claude has actually not been trained on, say, a mystifying and still poorly-explained Java concurrency bug I experienced in 2012, which cost a customer $150,000. Now in 2026 we have language-side tooling that mitigates that bug and Claude can actually help a lot with the rewrite. But we certainly don't have language tooling around the mysterious (but now perfectly well-explained) bug I experienced in 2017 around daylight saving's time and power industry peak/off-peak hours. I guess I haven't asked, but I can almost guarantee Claude would be no help there whatsoever.
Just so many confusing things go wrong in real-world software, and it is asinine to think that Mythos finding a ton of convoluted memory errors in legacy native code means we've solved debugging. People should pay more attention to the conclusion of "Claude builds a C compiler" - eventually it wasn't able to make further progress, the code was too convoluted and the AI wasn't smart enough. What if that happens at your company in 2027, and all the devs are too atrophied to solve the problem themselves?
I don't think we're "doomed" like some anti-AI folks. But I think a lot of companies - potentially even Anthropic! - are going to collapse very quickly under LLM-assisted technical debt.
> LLMs are absolutely causing us to lose something very important
The time wasted thinking our craft matters more than solving real world problems?
The amount of ceremony we're giving bugs here is insane.
Paraphrasing some of y'all,
> "I don't have to spend a day stepping through with a debugger hoping to repro"
THAT IS NOT A PROBLEM!
We're turning sand into magic, making the universe come alive. It's as if we just got electricity and the internet and some of us are still reminiscing about whale blubber smells and chemical extraction of kerosene.
The job is to deliver value. Not miss how hard it used to be and how much time we wasted finding obscure cache invalidation bugs.
Only algorithms and data structures are pure. Your business logic does not deserve the same reverence. It will not live forever - it's ephemeral, to solve a problem for now. In a hundred years, we'll have all new code. So stop worrying and embrace the tools and the speed up.
I mean to cause a stir! Let me invoke every logical fallacy and dirty rhetorical device I can if it draws attention.
Too many of our engineering conversations are dominated by veneration of the old. Let me be hyperbolic so that I can interrupt your train of thought and say this:
We're starting to live in the future.
Let go of your old assumptions. Maybe they still matter, but it's also likely some of them will change.
The old ways of doing things should be put under scrutiny.
In ten years we might be writing in new languages that are better suited for LLMs to manipulate. Frameworks and libraries and languages we use today might get tossed out the door.
All energy devoted to the old way of doing things is perhaps malinvested into a temporary state of affairs. Don't over-index on that.
This does depend on who you are; If you're a senior with 10+ years of experience, it's a failure of your abilities to cut your losses or know when to seek help if you take far too long debugging something.
But for juniors, it's invaluable experience. And as a field we're already seeing problems resulting from the new generations of juniors being taught with modern web development, whose complexity is very obstructing of debugging.
There are definitely situations where you can't ask for help and you can't turn your back on the bug.
I worked on a project that depended on an open source but deprecated/unmaintained Linux kernel module that we used for customers running RHEL[1]. There were a number of serious bugs causing panics that we encountered, but only for certain customers with high VFS workloads. I spent days to a week+ on each one, reading kernel code, writing userland utilities to repro the problem, and finally committing fixes to the module. I was the only one on the team up to the task.
We couldn't tell the customers to upgrade, we couldn't write an alternative module in a reasonable timeframe, and they paid us a lot of money, so I did what I had to do.
I'm sure there are lots of other examples like this out there.
[1] Known for its use of ancient kernels with 10000 patches hand-picked by Red Hat. At least at the time (5-10 years ago).
> We don’t have teachers or a curriculum, and there’s very little required structure beyond making a full-time commitment during your retreat
I saw this quote when looking at the Recurse Center website. How does one usually go about something like this if they work full time? Does this mainly target those who are just entering the industry or between jobs?
I know the article is mostly about what the author built at the coding retreat, but now he has me interested in trying to attend one!
Depressing. It's like reading has-been actors' stories about how they went to wellness retreats to "reconnect with themselves" to try get back on the job. I can't wait for the day when the same type of people as the author - or indeed, the author himself - start labeling plain regular programming as "artisanal" and "craft".
I wish more was being invested in AI autocomplete workflows. That was a nice middle-ground.
But yeah my hunch is "the old way" - although not sure we can even call it that - is likely still on par with an "agentic" workflow if you view it through a wider lens. You retain much better knowledge of the codebase. You improve your understanding over coding concepts (active recall is far stronger than passive recognition).
So we’ve already grown nostalgic for the old days… skimming through an alien looking codebase, scratching your head trying to figure what crazy abstraction the last person who touched this code had in mind. Oh shit it was me? That made so much more sense back then… but it’s been 6 hours and I can’t figure out why this does not work anymore. So you read some docs but they are poorly written. So you find something on Google and try to hack that into your solution. But nope, now more stuff broke. There goes your day.
I started using Zed as a half measure. I think I'll start using AI for planning and suggested implementation steps.
I am seeing non technical people getting involved building apps with Claude. After the Openclaw and other Agentic obsession trends I just don't see it pragmatic to continue down the road of AI obsession.
In most other aspects of life my skills were valuated because of my ability to care about details under the hood and the ability to get my hands dirty on new problems.
Curious to see how the market adapts and how people find ways to communicate this ability for nuance.
I've settled into a pattern of using agents for work (where throughput/results are the most important) and doing things the hard way for personal or learning projects (where the learning is more important).
It's easy to take for granted lots of experience programming before the advent of LLMs. This seems like a good strategy to develop understanding of software engineering.
I remember writing BASIC on the Apple II back when it wasn't retro to do so!
The fact that with AI development, your brain is no longer in a tight feedback loop with the codebase, leading to a significant drift between your model and reality, is still a sticking point with me and agentic development. It feels like trying to eat with silicone rubber chopsticks. I lose all precision and dexterity.
I still keep hoping there'll be a glut of demand for traditional software engineers once the bibbi in the babka goes boom in production systems in a big way:
But agentic workflows are so good now—and bound to get better with things like Claude Mythos—that programming without LLMs looks more and more cooked as a professional technique (rather than a curiosity or exercise) with each passing day. Human software engineers may well end up out of the loop completely except for the endpoints in a few years.
Old? OP showed a pic of an Apple IIe. I bought one for a few thousand bucks (I forget exactly how much). I've been an SE for 44 years. We just added the final abstraction layer.
That guy sounds so full of it just after reading the first few paragraphs... if had anywhere near this attitude before he got into LLMs, I would probably not have liked to be on a development team with him.
Here’s how i do it: I create a lot of stuff using AI to the max, but I also spend the necessary of time on reviewing that the AI is producing code that passes my cognitive load standards. this involves some tokens spent on grooming code and documenting well. Most of this is effortless thanks to an AGENTS.md based on this: https://github.com/zakirullin/cognitive-load/blob/main/READM... but i have a good sense of catching when things are getting weird and i steer back.
Then, when credits run out. It’s show time! The code is neatly organized, abstractions make sense, comments are helpful so I have a solid ground to do some good old organic human coding. I make sure that when i’m approaching limits I’m asking the AI to set the stage.
I used to get frustrated when credits ran out because the AI was making something I would need to study to comprehend. Now I’m eager to the next “brain time hand-out”
It sounds weird but it’s a form of teamwork. I have the means to pay for a larger plan but i’d rather keep my brain active.
I did things the old way for 25 years and my carpal tunnels are wearing out. LLMs let me produce the same quality I always have with a lot less typing so not mad at that at all. I review and own every line I commit, and feel no desire to go back to the old way.
What scares the shit out of me are all these new CS grads that admit they have never coded anything more complex than basic class assignments by hand, and just let LLMs push straight to main for everything and they get hired as senior engineers.
It is like hiring an army of accountants that have never done math on paper and exclusively let turbotax do all the work.
If you have never written and maintained a complex project by hand, you should not be allowed to be involved in the development of production bound code.
But also, I feel this way about the industry long before LLMs. If you are not confident enough to run Linux on the computer in front of you, no senior sysadmin will hire you to go near their production systems.
Job one of everyone I mentor is to build Linux from scratch, and if you want an LLM build all the tools to run one locally for yourself. You will be way more capable and employable if you do not skip straight to using magic you do not understand.
> It is like hiring an army of accountants that have never done math on paper and exclusively let turbotax do all the work.
It's not though. It's fundamentally different because TurboTax will still work with clear deterministic algorithms. We need to see that the jump to AI is not a jump from hand written math to calculators. It's a jump from understanding how the math works to another world of depending on magic machines that spit out numbers that sort of work 90% of the time.
From what I remember, typical new C++ debugged code speed is about 20-25K lines per year, lines that are non-blank, non-comment and not completely verifiable by compiler. E.g., standalone bracket or comma or semicolon are not lines of code, function header is too not a line of code, but computation, conditions and loops are. This is from old IBM statistics, I learned about it circa 2007.
If we assume that there are 50 weeks per year, this gives us about 400-500 lines of code per week. Even at long average 65 chars per line, it goes not higher than 33K bytes per week. Your comment is about 1250 bytes long, if you write four such comments per day whole week, you would exceed that 33K bytes limit.
Yes, I checked their history of comments before posting. It made me confident that I hit the right note.
My software engineering experience longs almost 37 years this year, six-to-seven years more than Earth's human population median age. I had two burnouts through that time, but no carpal tunnel syndrome symptoms at all. When I code, I prefer to factor subproblems out, it reduces typing and support costs.
> If you have never written and maintained a complex project by hand, you should not be allowed to be involved in the development of production bound code.
So only the old hands allowed from now on, or how are we going to provide these learning opportunities at scale for new developers?
Junior developers have always been a lot less effective than senior developers. We will need new senior developers so we will need to train junior developers. Maybe we train them by forcing them to do things the hard way. The slow way. By hand. Because if we let them do things the fast way they are going to cause some serious damage.
Employers were already refusing to hire juniors, even when 0.5-1 years' salary for a junior would be cheaper than spending the same on hiring a senior.
They'll never accept intentionally "slower" development for the greater good.
my last summer intern did everything the manual way, except for a chunk where I wanted him to get something done fast without having to learn all the underlying chunks
The same way I learned 25 years ago still works today. Volunteer on open source projects.
Always happy to mentor people at stagex and hashbang (orgs I founded).
Also being a maintainer of an influential open source project goes on a resume, and helps you get seen in a crowded market while boosting your skills and making the world better. Win/win all around.
Even by pessimistic progress projections AI will be better than most at coding before this is a long term issue. And the output multiplier I'm seeing I suspect the number of SWEs needed to achieve the same task is going to start shrinking fast.
I don't think SWE is a promising career to get started in today.
But pro-AI posts never seem to pin themselves down on whether code checked in will be read and understood by a human. Perhaps a lot of engineers work in “vibe-codeable” domains, but a huge amount of domains deal with money, health, financial reporting, etc. Then there are domains those domains use as infrastructure (OS, cloud, databases, networking, etc.)
Even where it is non-critical, such as a social media site, whether that site runs and serves ads (and bills for them correctly) is critical for that company.
you dont notice it when you are only looking at your own harness results, but the llm bakes so very much of your own skills and opinions into what it does.
But you have to be good at SWE to be good at security engineering and sysadmin, and the demand there is skyrocketing.
We have a completely broken internet with almost nothing using memory encryption, deterministic builds, full source bootstrapping, secure enclaves, end to end encryption, remote attestation, hardware security auth, or proper code review.
Decades of human cognitive work to be done here even with LLM help because the LLMs were trained to keep doing things the old way unless we direct them to do otherwise from our own base of experience on cutting edge security research no models are trained on sufficiently.
> It is like hiring an army of accountants that have never done math on paper and exclusively let turbotax do all the work.
That is exactly been the situation for years. Once graduated accountants are not doing maths. They are using software (Exel, Xero etc.). They do need to know some basic formulas eg. NPV.
What they need to know is the law, current business practices etc.
> LLMs let me produce the same quality I always have with a lot less typing
If that's true, then you likely used to produce slop for code. :-(
> I did things the old way for 25 years and my carpal tunnels are wearing out.
You wrote so much code as to wear out your carpal tunner? Are you sure it isn't the documentation and the online chatter with your peers? :-(
... anyway, I know it's corny to say, but - you should have, and shoudl now, improve the ergonomics of your setup. Play with things like the depth of your keyboard on your desk, the height of the chair and the desk, with/without chair handrests, keyboard angle, etc.
> Job one of everyone I mentor is to build Linux from scratch
I mean, that's the only way I code. I don't use llm's to do my work for me. I'm perfectly capable of solving any sort of problem on my own, and then I'll understand it well enough to explain it to someone later.
You should do what you want, and as a break it’s fine. But IMO right now the most leverage for most people is learning how to effectively manage agents. It’s really hard. Not many are truly good with it. It will be relevant for a long time.
Why would you think that? The landscape is fast-moving. Prompting tricks and "AI skills" of yesterday are already dated and sometimes actively counterproductive. The explicit goal of the companies working on the tech is to lower the barriers to entry and make it easier to use, building harnesses and doing refinement that align LLMs to an intuitive mode of interaction.
Do you think they'll fail? Do you think we've plateaued in terms of what using a computer looks like and your learnings for wrangling the agents of this year will be relevant for whatever the new hotness is next year? It's a strong claim that demands similarly strong argument to support.
I believe the point (which you seem to tacitly agree with) is that a young dev's time is much better spent reading and writing code "the old-fashioned way" vs chasing the new SOTA in AI-assisted development. A competent dev can basically master agentic development in a few months. But it takes years to become competent.
Not sure what you are using, but that’s easier said than done. I just set up an agent to ensure that my other agent would follow my coding guidelines by using hooks. The coding agent responded by switching to editing with `sed`, etc. to circumvent the hooks.
Claude Opus is going to give zero fucks about your attempts to manage it.
this is exactly right, I don't manage agents anymore (and have spent countless hours before learning how to do so, now this is a skill like my microsoft access skills (which were amazing back in the day...)
It is hard indeed. I find it really quite exhausting.
Personally, I feel like I have always been a very competent programmer. I'm embracing the new way of working, but it seems like quite a different skillset. I somewhat believe that it will be relevant for a long time, because there is an incredibly large gap in outcomes between members of my team using AI. I've had good results so far, but I'm keen to improve.
Yeah, it's really difficult to remember to tell it "make no mistakes". Typing a prompt is also really hard, especially when you have to remember the cli command to open the agent. Sometimes I even forget if I need to use "medium", "high", or "xhigh" for a task.
I see you got downvoted by I agree. I went through a massive valley of despair and turned back to hand crafting only to realize that for me coding was always a means to an end and I really didn't care at all about how I got there. Now I'm having a lot of fun building out all kinds of wonky projects.
> There were 2 or 3 bugs that stumped me, and after 20 min or so of debugging I asked Claude for some advice. But most of the debugging was by hand!
Twenty whole minutes. Us old-timers (I am 39) are chortling.
I am not trying to knock the author specifically. But he was doing this for education, not for work. He should have spent more like 6 hours before desperately reaching for the LLM. I imagine after 1 hour he would have figured it out on his own.
* Ask someone to come over and look
* Come back the next day, work on something else
* Add comment # KNOWN-ISSUE: ...., and move on and forget about it.
But year spent days on a bug at work before ha ha!
This is a tried and true way of working on puzzles and other hard problems.
I generally have 2-4 important things in flight, so I find myself doing this a lot when I get stuck.
But just today a bug was reported by a customer (we are still in testing not a production bug). I implemented this project myself from an empty git repo and an empty AWS account including 3 weeks of pre implementation discovery.
I reproduced the issue and through the problem at Claude with nothing but two pieces of information - the ID of the event showing the bug and the description.
It worked backwards looking at the event stream in the database, looking at the code that stored the event stream, looking at the code that generated the event stream (separate Lambda), looking at the actual config table and found the root cause in 3 minutes.
After looking at the code locally, it even looked at the cached artifacts of my build and verified that what was deployed was the same thing that I had locally (same lambda deployment version in AWS as my artifacts). I had it document the debug steps it took in an md file.
Why make life harder on myself? Even if it were something I was doing as a hobby, I have a wife who I want to spend time with, I’m a gym rat and I’m learning Spanish. Why would I waste 6 hours doing something that a computer could do for me in 5 minutes?
Assuming he has a day job and gets off at 6, he would be spending all of his off time chasing down a bug that he could be using doing something else.
Though a lot of the time this is more an inefficiency of the documentation and Google rather than something only LLMs could do.
Having a tool that instantly searches through the first 50 pages of google and comes up with a reasonable solution is just speeding up what I would have done manually anyways.
Would I have learned more about (and around) the system I‘m building? Absolutely. I just prefer making my system work over anything else, so I don’t mind losing that.
Just so many confusing things go wrong in real-world software, and it is asinine to think that Mythos finding a ton of convoluted memory errors in legacy native code means we've solved debugging. People should pay more attention to the conclusion of "Claude builds a C compiler" - eventually it wasn't able to make further progress, the code was too convoluted and the AI wasn't smart enough. What if that happens at your company in 2027, and all the devs are too atrophied to solve the problem themselves?
I don't think we're "doomed" like some anti-AI folks. But I think a lot of companies - potentially even Anthropic! - are going to collapse very quickly under LLM-assisted technical debt.
The euphoria I felt after fixing bugs that I stayed up late working on is like nothing else.
The time wasted thinking our craft matters more than solving real world problems?
The amount of ceremony we're giving bugs here is insane.
Paraphrasing some of y'all,
> "I don't have to spend a day stepping through with a debugger hoping to repro"
THAT IS NOT A PROBLEM!
We're turning sand into magic, making the universe come alive. It's as if we just got electricity and the internet and some of us are still reminiscing about whale blubber smells and chemical extraction of kerosene.
The job is to deliver value. Not miss how hard it used to be and how much time we wasted finding obscure cache invalidation bugs.
Only algorithms and data structures are pure. Your business logic does not deserve the same reverence. It will not live forever - it's ephemeral, to solve a problem for now. In a hundred years, we'll have all new code. So stop worrying and embrace the tools and the speed up.
This is both a strawman and a false dichotomy.
Too many of our engineering conversations are dominated by veneration of the old. Let me be hyperbolic so that I can interrupt your train of thought and say this:
We're starting to live in the future.
Let go of your old assumptions. Maybe they still matter, but it's also likely some of them will change.
The old ways of doing things should be put under scrutiny.
In ten years we might be writing in new languages that are better suited for LLMs to manipulate. Frameworks and libraries and languages we use today might get tossed out the door.
All energy devoted to the old way of doing things is perhaps malinvested into a temporary state of affairs. Don't over-index on that.
If you cant fix the bug just slop some code over it so its more hidden.
This is all gonna be fascinating in 5-10 years.
But for juniors, it's invaluable experience. And as a field we're already seeing problems resulting from the new generations of juniors being taught with modern web development, whose complexity is very obstructing of debugging.
I worked on a project that depended on an open source but deprecated/unmaintained Linux kernel module that we used for customers running RHEL[1]. There were a number of serious bugs causing panics that we encountered, but only for certain customers with high VFS workloads. I spent days to a week+ on each one, reading kernel code, writing userland utilities to repro the problem, and finally committing fixes to the module. I was the only one on the team up to the task.
We couldn't tell the customers to upgrade, we couldn't write an alternative module in a reasonable timeframe, and they paid us a lot of money, so I did what I had to do.
I'm sure there are lots of other examples like this out there.
[1] Known for its use of ancient kernels with 10000 patches hand-picked by Red Hat. At least at the time (5-10 years ago).
I saw this quote when looking at the Recurse Center website. How does one usually go about something like this if they work full time? Does this mainly target those who are just entering the industry or between jobs?
I know the article is mostly about what the author built at the coding retreat, but now he has me interested in trying to attend one!
But yeah my hunch is "the old way" - although not sure we can even call it that - is likely still on par with an "agentic" workflow if you view it through a wider lens. You retain much better knowledge of the codebase. You improve your understanding over coding concepts (active recall is far stronger than passive recognition).
You mean the way that the majority of code is still written by professionals?
I do the former for fun. The latter to provide for my family.
There is a reason old men take on hobbies like woodworking and fixing old cars and other stuff that has been replaced by technology.
I am seeing non technical people getting involved building apps with Claude. After the Openclaw and other Agentic obsession trends I just don't see it pragmatic to continue down the road of AI obsession.
In most other aspects of life my skills were valuated because of my ability to care about details under the hood and the ability to get my hands dirty on new problems.
Curious to see how the market adapts and how people find ways to communicate this ability for nuance.
I remember writing BASIC on the Apple II back when it wasn't retro to do so!
I still keep hoping there'll be a glut of demand for traditional software engineers once the bibbi in the babka goes boom in production systems in a big way:
https://m.youtube.com/watch?v=J1W1CHhxDSk
But agentic workflows are so good now—and bound to get better with things like Claude Mythos—that programming without LLMs looks more and more cooked as a professional technique (rather than a curiosity or exercise) with each passing day. Human software engineers may well end up out of the loop completely except for the endpoints in a few years.
> 15 years of Clojure experience
My God I’m old.
Then, when credits run out. It’s show time! The code is neatly organized, abstractions make sense, comments are helpful so I have a solid ground to do some good old organic human coding. I make sure that when i’m approaching limits I’m asking the AI to set the stage.
I used to get frustrated when credits ran out because the AI was making something I would need to study to comprehend. Now I’m eager to the next “brain time hand-out”
It sounds weird but it’s a form of teamwork. I have the means to pay for a larger plan but i’d rather keep my brain active.
What scares the shit out of me are all these new CS grads that admit they have never coded anything more complex than basic class assignments by hand, and just let LLMs push straight to main for everything and they get hired as senior engineers.
It is like hiring an army of accountants that have never done math on paper and exclusively let turbotax do all the work.
If you have never written and maintained a complex project by hand, you should not be allowed to be involved in the development of production bound code.
But also, I feel this way about the industry long before LLMs. If you are not confident enough to run Linux on the computer in front of you, no senior sysadmin will hire you to go near their production systems.
Job one of everyone I mentor is to build Linux from scratch, and if you want an LLM build all the tools to run one locally for yourself. You will be way more capable and employable if you do not skip straight to using magic you do not understand.
It's not though. It's fundamentally different because TurboTax will still work with clear deterministic algorithms. We need to see that the jump to AI is not a jump from hand written math to calculators. It's a jump from understanding how the math works to another world of depending on magic machines that spit out numbers that sort of work 90% of the time.
If we assume that there are 50 weeks per year, this gives us about 400-500 lines of code per week. Even at long average 65 chars per line, it goes not higher than 33K bytes per week. Your comment is about 1250 bytes long, if you write four such comments per day whole week, you would exceed that 33K bytes limit.
I find this amusing.
My software engineering experience longs almost 37 years this year, six-to-seven years more than Earth's human population median age. I had two burnouts through that time, but no carpal tunnel syndrome symptoms at all. When I code, I prefer to factor subproblems out, it reduces typing and support costs.
So only the old hands allowed from now on, or how are we going to provide these learning opportunities at scale for new developers?
Serious question.
Employers were already refusing to hire juniors, even when 0.5-1 years' salary for a junior would be cheaper than spending the same on hiring a senior.
They'll never accept intentionally "slower" development for the greater good.
my last summer intern did everything the manual way, except for a chunk where I wanted him to get something done fast without having to learn all the underlying chunks
Always happy to mentor people at stagex and hashbang (orgs I founded).
Also being a maintainer of an influential open source project goes on a resume, and helps you get seen in a crowded market while boosting your skills and making the world better. Win/win all around.
I don't think SWE is a promising career to get started in today.
But pro-AI posts never seem to pin themselves down on whether code checked in will be read and understood by a human. Perhaps a lot of engineers work in “vibe-codeable” domains, but a huge amount of domains deal with money, health, financial reporting, etc. Then there are domains those domains use as infrastructure (OS, cloud, databases, networking, etc.)
Even where it is non-critical, such as a social media site, whether that site runs and serves ads (and bills for them correctly) is critical for that company.
you dont notice it when you are only looking at your own harness results, but the llm bakes so very much of your own skills and opinions into what it does.
LLMs still regurgitate a ton.
We have a completely broken internet with almost nothing using memory encryption, deterministic builds, full source bootstrapping, secure enclaves, end to end encryption, remote attestation, hardware security auth, or proper code review.
Decades of human cognitive work to be done here even with LLM help because the LLMs were trained to keep doing things the old way unless we direct them to do otherwise from our own base of experience on cutting edge security research no models are trained on sufficiently.
That is exactly been the situation for years. Once graduated accountants are not doing maths. They are using software (Exel, Xero etc.). They do need to know some basic formulas eg. NPV.
What they need to know is the law, current business practices etc.
If that's true, then you likely used to produce slop for code. :-(
> I did things the old way for 25 years and my carpal tunnels are wearing out.
You wrote so much code as to wear out your carpal tunner? Are you sure it isn't the documentation and the online chatter with your peers? :-(
... anyway, I know it's corny to say, but - you should have, and shoudl now, improve the ergonomics of your setup. Play with things like the depth of your keyboard on your desk, the height of the chair and the desk, with/without chair handrests, keyboard angle, etc.
> Job one of everyone I mentor is to build Linux from scratch
"from scratch" can mean any number of things.
Why would you think that? The landscape is fast-moving. Prompting tricks and "AI skills" of yesterday are already dated and sometimes actively counterproductive. The explicit goal of the companies working on the tech is to lower the barriers to entry and make it easier to use, building harnesses and doing refinement that align LLMs to an intuitive mode of interaction.
Do you think they'll fail? Do you think we've plateaued in terms of what using a computer looks like and your learnings for wrangling the agents of this year will be relevant for whatever the new hotness is next year? It's a strong claim that demands similarly strong argument to support.
How? I just open multiple terminal panes, use git tree, and then basically it’s good old software dev practices. What am I missing?
Claude Opus is going to give zero fucks about your attempts to manage it.
It is hard indeed. I find it really quite exhausting.
Personally, I feel like I have always been a very competent programmer. I'm embracing the new way of working, but it seems like quite a different skillset. I somewhat believe that it will be relevant for a long time, because there is an incredibly large gap in outcomes between members of my team using AI. I've had good results so far, but I'm keen to improve.
For the good stuff, there’s no alternative but to know and to have taste. Llms change nothing.
Citation needed.