Though a lot of the time this is more an inefficiency of the documentation and Google rather than something only LLMs could do.

This can be set as far as 1h of being stuck. Can also be 5 minutes. But by default it is 30 seconds.

My inner kid was screaming "that's cheating!" :-D but on second thought it is a very cool feature for us busy adults, however it's sad the extremes that gamedevs have to go in order to appease the short-term mindless consumers of today's tik-toks.

But more seriously, where's the joy of generating long-standing memories of being stuck for a while on a puzzle that will make you remember that scene for 30 years? An iconic experience that separates this genre from just being an animated movie with more steps.

I couldn't imagine "Monkey Island II but every 30 seconds we push you forward". Gimme that monkey wrench.

TFA and this comment just made me have this thought about today's pace of consumption, work, and even gaming.

* 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!

If anyone remembers middleschool mathematics this is the coding example of the teacher making you write out the equations in their longest form instead of shortcutting. It's done this way because it shows you your exact train of thought and where you went wrong. That sticks in your head. You understand the problem by understanding yourself. Giving up after twenty minutes instead of stopping, clearing your active cognitive load, and then coming back erases your ability to understand that train of thought.

For a comparison it's like being in first person view in a videogame, and the only thing you have is the ability to look behind you, versus being able to bring up a map that has an overhead view. In first person you're likely to lose where exactly you went to get where you are, while with the overhead view map you can orient your traveled route according to landmarks and distance.

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.

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?

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.

Now, they are programming a chip from the seventies using an editor/assembler that was written in 1983 and has a line editor, not a full-screen one.

We had a total of 10 hours of class + lab where I taught them about assembly language and told them about the registers, instructions, and addressing modes of the chip, memory map and monitor routines of the Apple, and after that we went and wrote a few programs together, mostly using the low-resolution graphics mode (40x40): a drawing program, a bouncing ball, culminating in hand-rolled sprites with simple collision detection.

Their assignment is to write a simple program (I suggested a low-res game like Snake or Tetris but they can do whatever they want provided they tell me about it and I okay it), demo their program, and then explain to the class how it works.

At first they hated the line editor. But then a very interesting thing happened. They started thinking about their code before writing it. Planning. Discussing things in advance. Everything we told them they should do before coding in previous classes, but they didn't do because a powerful editor was right there so why not use it?...

And then they started to get used to the line editor. They told me they didn't need to really see the code on the screen, it was in their head.

They will of course go back to modern tools after class is finished, but I think it's good for them to have this kind of experience.

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).

But when it comes to the final act I find myself unwilling to let an LLM write the actual code - I still do it myself.

Perhaps because my main project at the moment is a game I've been working on for four years, so the codebase is sizable, non-trivial, and all written by me. My strong sense even since coding LLMs showed up has been that continuing to write the code is important for keeping it coherent and manageable as a whole, including my mental model of it.

And also: for keeping myself happy working on it. The enjoyment would be gone if I leaned that far into LLMs.

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 appreciate that the author understands why doing everything "the old way" is good. AI is a tool, it can't be a replacement for how you think and it can't be a replacement for the actual work.

I wish more people had a desire for the inner workings of things because it makes you better at actually using tools. Implementing compilers, databases, OSes, control systems, etc. is like practicing swimming. Yeah, you might not ever swim again but when you need to the muscle memory will be there when you need to get out of the ocean (I know this is a strained metaphor).

Knowing more can only be a boon to using LLMs for coding and it's really a general problem in ML. I work in a science field as hw / sw engineer and I've seen so many pure data science people say they can replace all our work with a model, flail for 2 years and then their whole org gets canned. If they just read a textbook or collaborated (which they never do, no matter how polite you are), they'd have been able to leverage their data science skills to build something great and instead they just toil away never making it past step 0.

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.

I’ve spent a lifetime teaching myself programming, computers, and engineering. I have no formal education in these disciplines and find that I excel due to the self-taught nature of my background.

I take a very metered approach to AI and use it for autocomplete while still scrutinizing every token (not the AI kind) as well as an augment to my self-pedagogy. It’s great to be able to “query” or get a summary from a set of technical documents on demand.

However, I don’t understand the desire to remove oneself from the process with AI. I simply don’t do anything that won’t teach me something new or improve my existing skills.

There’s more to engineering than simply programming. Both the engineer and the intended user base must also understand the system. The value lost is greater than the sum of all the parts when an LLM produces most or all of the code.

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 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!

> One solution to this constant companion problem: Spend more time with your phone out of easy reach. If it’s not nearby, it won’t be as likely to trigger your motivational neurons, helping clear your brain to focus on other activities with less distraction.

Reminds me of this study: "The mere presence of a smartphone reduces basal attentional performance"

The effect persisted even when the phone was switched off. It only went away when the phone was moved to a different part of the building.

https://www.nature.com/articles/s41598-023-36256-4

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.

fine, but the gym analogy breaks down somewhere. in a gym, the person who actually lifts heavier gets noticed. in software, the person with the right bio and the right network gets noticed, regardless of whether they've ever lifted anything real.

you can spend three years learning compilers properly and have a handful of readers. someone else ships a wrapper on a saturday and lands a pmarca quote tweet by monday.

coding the old way is good for you. i'm not convinced it's what gets you noticed. the strain was never really what got rewarded in the first place.

The old way?! So not using AI is already being called "the old way"?!!

That statement sends alarm bells off about writing on the internet and trust to be put into it as if I'm the first one to notice it.

1. It increases the chances of any bugs being found and resolved.

2. It encourages the author to be more careful with their code to avoid long reviews with a lot of findings.

3. It ensures at least two people - the author and approverd - have familiarity with the code.

4. It spreads responsibility for the code across at least two people - the author and approvers.

It's clear this article's author does not review their own code. I sure hope that code is not used for anything important.

I hope if/when diffusion models get a little more traction down the line it'll put some new life into autocomplete(-adjacent) workflows. The virtually instantaneous responses of Inception's Mercury models [0] still feel a little like magic; all it's missing is the refinement and deep editor integration of Cursor.

On the subject of diffusion models, it's a shame there aren't any significant open-weight models out there, because it seems like such a perfect fit for local use.

[0] https://www.inceptionlabs.ai/

When I let an agent write too much of the structure, the code may work, but a week later every small change starts with "where did it put that?"

That's an interesting thing to include. I agree with this point in principle, but I've found that Claude, at least, duplicates logic FAR too often and needs nudging in the other direction.

I can't do it. If I let an LLM write code for me, that code is untouchable. I see it as a black box, that I will categorically refuse to open. If it works, I use it, but don't trust it. If it breaks, I get frustrated. The only way that works for me is me behind the driving wheel at all times and an LLM as an assistant that answers my questions. We either brainstorm something or it helps me express things I know in languages syntax. Somehow that step has always been a bit of a burden for me - I understood the concepts well, but expressing them in syntax was a bit of a difficulty.

Someone thought I was naive when I said my vibe coded internal web admin site met the security requirements without looking at a line of code.

I knew that because the requirements were that anyone who had access to the site could do anything on the site and the site was secured with Amazon Cognito credentials and the Lambda that served it had a least privileged role attached.

If either of those invariants were broken, Claude has found a major AWS vulnerability.

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.

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.

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.