Most of the non-technical people I see using LLMs to create software are not engaging in "cognitive surrender" as far as I can tell.
They didn't read the code before and they don't now. However, asking questions and improving understanding of the system and architecture is actually faster and easier for them now than it used to be.
They're evaluating what they can see, i.e, judging results. They build things for their own use. They test them, iterate on them to meet their needs and try to achieve their vision. This is roughly the same process they followed when they used to outsource to an agency except the loop is much tighter.
It seems to work well, mainly hurts when the problems are invisible, so security, backups, breaking migrations, perf, maintenance over time (language / runtime updates, dep updates, server updates etc). All the stuff that hurts if you have never been responsible for the uptime of something before. Not knowing that software is never "done". But junior devs trip on those exact same things.
I agree with the tail of the article; tooling and processes that make that stuff more visible would close a lot of the gaps.
If we continue from your "water comes from the tap" example this situation feels like someone who has no experience of plumbing deciding they're going to replace their bathroom without bothering to find out how any of it works. They'll possibly end up with a bathroom that superficially works but they're also liable to find problems down the line with leaks, things not draining properly, or water pressure issues.
Some of the people writing software purely via LLMs are even turning this into a business, which I guess would be analogous to deciding you're going to run sewage treatment for a small city without understanding anything about how that works. It'll mainly hurt when the problems are invisible, so food poisoning, or inlets backing up and overflowing into a nearby river.
In the bathroom remodel analogy, the AI is the plumber that you hire to do the work for you. You tell it what you want, it figures out the details necessary to get it to you. If what you want isn't possible, or requires large changes, a good plumber will raise the issue to you and explain the constraints they are working under, ask for guidance, and offer recommendations.
Sometimes the AI does things in a weird or inefficient way, or neglects details that become important later, or misunderstands something important about the task resulting in an outcome other than what you wanted, but that's also the same as the plumber. This is why people usually want to be at home and keep an eye on the people working on their improvement projects or repairs, because they want to make sure that the guy they hired is replacing the broken faucet, not the functional but odd-looking one.
There is still value in knowing the underlying systems well enough to present a good design vision to the plumber up-front. It's still possible to have a competent executor produce a bad idea, which is how we get stuff like Groverhaus, the Doom Bathroom, and McMansion Hell, which are examples of "slop architecture" in exactly the same way as we are seeing "slop software".
I agree it's pretty bloody cheeky to run an agency like that though. The only ways out I can see are licensing or real liability.
But let he who has never done dodgy home improvements cast the first stone.
People looking for self-contained, spot solutions to localized problems will have much more capacity to solve them locally, while people with in-depth expertise will be able to build systems that are even more reliable, performant, and scalable than what they were building before.
So we'll get the benefits of disintermediation for the basics, and much greater capacity to deal with effectively with complexity for the not-so-basics.
One of the things I've built in my current role is tooling to help understand current architecture, and to resolve the differences between things as-documented, as-implemented, and as-actually-behaving. It's used for a few things, including updating documentation, but it's been most impactful when onboarding new engineers. They can simply ask questions about how things work and why, and get explanations and deep dives wherever they need it.
Is the article claiming that?
> Not knowing all the details seems more like the default human condition, in the sense that most people don't know exactly where their water comes from or where their waste goes exactly. "Water comes from the tap, food comes from the store".
Sure. The ability to specialize, and on the flip-side not have to care about a lot of things, remains one of the biggest driving forces of human civilization. But in this article, I don't think the person worried about cognitive surrender is the "most people" in relation to food or water. They're the farmer or the water infrastructure engineer! They absolutely for sure do need to know those things. And all around us we see a lure of systems that invite them to surrender.
> Most of the non-technical people I see using LLMs to create software are not engaging in "cognitive surrender" as far as I can tell.
Maybe not. But the might be cognitively surrendering when they use the LLM to summarize the day's news or write a message to a friend, or even plan dinner for the week. I'd say they are. I agree that it's not clear-cut.
> They're evaluating what they can see, i.e, judging results. They build things for their own use. They test them, iterate on them to meet their needs and try to achieve their vision.
No. They have things built, then they test them. This is different. It might be OK, but it's not the same as building.
> This is roughly the same process they followed when they used to outsource to an agency except the loop is much tighter.
Yes. But they wouldn't usually outsource summarizing a letter from grandma, or the political agenda of a candidate/party in an upcoming election. That's surrender.
> It seems to work well, mainly hurts when the problems are invisible, so security, backups, breaking migrations, perf, maintenance over time (language / runtime updates, dep updates, lib updates, server updates etc).
… or ones own abilities deteriorate.
> Tooling and processes that make that stuff more visible would close a lot of the gaps.
Not the problem of one's own skills though.
Fair. Maybe I "cognitively surrendered" a long time ago just as a consequence of working in large teams :(
I hit the breaking point of not being able to read all the code well before LLMs came along. Also people would leave and the documentation for their subsystems would be wrong or incomplete, so there were always mysteries to chase down (during an incident, naturally) even in code written by hand.
Gets to the point, you don't exactly read the MRs, you're mainly checking their approach against what you talked about earlier and making sure it doesn't look way more gnarly than it's supposed to.
Working with agents doesn't feel that different.
> No. They have things built, then they test them. This is different. It might be OK, but it's not the same as building.
It does bug me sometimes when people say "yeah we built the deck..." and they got a contractor to do it. But then we do say things like "Nolan made the film" even though he didn't hold a camera or build the sets. To me, one-shotting doesn't feel like building, but engaging in a loop and applying control to get the outcome you want, does. Regardless of the exact toolset.
I don't personally see super hard lines between using a compiler, a 4GL, a sql query, stitching together 10 libraries and a framework I didn't write, using an LLM or even directing a team.
> Not the problem of one's own skills though.
I read somewhere once we can't weld Saturn V engines anymore, or at least not economically. We would also probably have a lot of trouble finding people who could do all the engine calcs the way they did. It doesn't necessarily mean that rocket progress is stalled.
> Yes. But they wouldn't usually outsource summarizing a letter from grandma, or the political agenda of a candidate/party in an upcoming election. That's surrender.
Agree 100% on the letter from Grandma.
I do say that, too. But I'm not in the film industry. My understanding is that those who are, don't use words as crude as "made" to describe a role in the making of a film. I believe they say that he wrote, directed or produced a film.
We are not talking about some other field of expertise here, where we are outsiders – the production of food, the supply of water, or the making of movies – we're talking about our own field of expertise.
Oh, no, film insiders use ‘made’ constantly, or even cruder - attribute credit without using a verb at all. Saying it’s a “Nolan film” is super common. Here’s a VFX person saying “Nolan’s CGI”: https://youtu.be/AFLcP-t0-kI (I’m ex-film industry, and still film industry tangential, and found this clip being discussed on a film industry mailing list.)
Film people are far more likely to pay attention to who the director is than non-film people, and it’s very common for film people to talk in terms of directors ‘making’ the film or otherwise casually assign ownership. I think this is especially true for famous directors and good films, and maybe a bit less true for unknown directors and mediocre films.
They didn't read code before, they just wrote it? That makes no sense.
Previously, pretty much everyone all agreed on YAGNI and KISS because code was so expensive to create and more expensive to maintain. Now the code generation is "free" - but at a design level, YAGNI and KISS do still matter. But they are enforced now only by sheer will power. You have to actively restrain the code from being created if you don't want it. Perfectly good code - I can't even fault it. I just know, if it doesn't need to exist yet, it's a mistake to create it earlier than needed.
> La perfection est atteinte non pas lorsqu'il n'y a plus rien à ajouter, mais lorsqu'il n'y a plus rien à retirer.
> Perfection is attained not when there is nothing more to add, but when there is nothing more to remove.
On the same theme:
> the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience
- Einstein
> I would have written a shorter letter, but did not have the time
- Pascal
> Simplicity is a great virtue but requires hard work to achieve and education to appreciate, and, to make matters worse, complexity sells better.
- Dijkstra.
Now would be the ideal time for everybody to give it negative feedback when bad code is returned but I'm afraid not enough people care and we'll just settle with what works just like we always have.
The design machines aren't good enough yet; and their progress has little relationship to LLMs at all. Designing computer programs and the proper level of abstraction for the easiest maintenance, flexibility and debugging is simply an open problem. There's nothing to train LLMs on that is smaller than the size of an entire program, and its history of code changes.
Get a few of those, figure out a way to analyze and reduce the patterns and lessons in them, and train on that maybe. Until then, there's no way for an LLM to figure out what lacking a particular abstraction is going to end up costing.
You can choose to ignore the fine details and just tell it what to build, or you can spend more time and effort and tell it how to do things, you can ask it to create a plan, review the plan and ask for changes etc, you can review the implementation and ask for changes there. You can simply tell it you don't like how a feature is implemented and ask it for suggestions on how to improve it, include what you don't like about it so it has direction.
It's all about the effort you put in. LLMs are able to write clean beautiful and efficient code, they just can't necessarily do exactly what you want on the first try without any direction. That's why you should figure out what you want, ask for what you want and make sure it's actually building what you want.
When endless bootstrapping and framing is free, you end up generating the most beautiful scaffold while ignoring the building behind the scaffold.
Keeping the volume of code down is definitely a challenge though.
At the same time I can't help but wonder that this is almost too much for us to cope, the LLMs are _too_ easy to use. I think we're very good at pretending there isn't a problem here but there is definitely going to be some cognitive decline happening.
We want to pretend like reading over the output of an LLM is nearly the same as us making those changes ourselves but I think there's a lot of value in those boring and manual steps. And most people (including me) will be too lazy to not eventually offload most things to the LLMs.
In a way this is similar to how watching a lecture tends to give one an "impression" of understanding that doesn't correlate with reality. We need hands-on work and some course work to get our monkey brains engaged.
I can only imagine what effects this is going to have in education.
Our cognition didn't evolve to produce arguments for online discussions, but for making crude tools and hunting. As we stopped applying our intelligence towards these uses as a result of our tools, we simply shifted it to new problems that were unthinkable previously.
A caveman probably wouldn't care too much about algebra, a topic most children learn today.
We should stop thinking about the problems AI trivialized and, under the assumption that AI won't fully replace human capability (a key assumption), start thinking about the problems that were previously out of reach. Education would then start focusing on that.
AI is not free. Even "free" and open source models require complex silicon to operate. The silicon and copper is assembled in complex fabrication plants. Those plants are very close to geopolitical belligerents
What I resist is the dependence. I'm not willing to trade short term productivity, for long term dependence
Taking clothing for example, there's more available now than ever. It's disposable at great cost to the environment and the workers that produce it, but very little to you.
Yet, I bet you cannot evaluate garments, construction, or fabric quality. You may not know how to wash woolens and linens. You may have only worn smelly plastic workout clothing and plastic suits for formal events.
You may never have worn a piece of clothing that actually fit your body.
You may not realize that you can alter any clothing, that most clothing was intended to be altered once upon a time. Or that older garments were both more expensive and higher quality, because the expectation was the most important items would need to last for a decade.
The Luddite's central objections about automation was about declining working conditions (objecting to child labour, for instance), bad pay, and declining quality. They used the 'obnoxious' machines they broke. They saw what was being lost and demanded better (without demanding no machines at all).
Later, the rise of unionization brought about many of the changes they'd asked for decades before.
I hope this current technologic movement doesn't end where fast fashion has, with influencers buying a new outfit [repo] every day for a year. A vortex of undifferentiated slop for its own sake.
I can also recommend the book "Blood in the Machine" by Brian Merchant [0][1] which is precisely about this.
[0] https://www.hachettebookgroup.com/titles/brian-merchant/bloo...
When I'm working on my own, I scrutinize every line of backend code and frequently send the LLM back for rework. Frontend code I keep an eye on just to make sure the LLM isn't getting stupid (which it occasionally does, even at the frontier), but mostly defer. The CSS I don't even look at.
This isn't too different from having employees. How much do I review other people's work? Depends on the employee, depends on how critical the work is.
It'll take a particularly dumb and lazy AI to replace me.
There’s one thing that’s bothering me though — all of these have us consuming what LLMs produce. I learned and internalized how things worked by building systems. At least within the constraints of the abstractions I was using. This makes me think of TikTok and shorts. Everybody is just watching. Maybe that’s not charitable though. Maybe the abstraction has now changed from an “os/gui/event loop” to “llm” and the rest is just accidental complexity.
Automatic control is good for standard case, and the expected errors. For weird cases, for now humans are better to detect them and press the big red button.
the process of surrendering to the machine god..
But.. we do monitor whether our prayers are effective .. right? Is that a smitable offense?
After that disappointment, I too was "curious": was the author going to talk about "curiosity", that magic virtue that we hackers believe, lifts design into the divine (but more often than not kills the pussy)
The best known example is the Japanese method of "Pointing and Calling/Naming" in their Train safety protocols but has been adapted to other industries too - https://en.wikipedia.org/wiki/Pointing_and_calling
These fall under "Behaviour Based Safety" (BBS) training methodologies and use techniques like "Check-backs", "Call-outs", "STAR" etc.
Think of platforms like Shopify. Shopify allows huge number of external users to write code customizing their web shops. But Shopify engineers do not need to review and understand the users' code, because there is a boundary which limits what the external code can do. The external code can be A Big Ball of Mud without making the whole platform a A Big Ball of Mud.
That said, the core apps are still full of AI code. I don't see a reality where hand-written code can or will be curated and sequestered. It's unenforceable, too slow, and too expensive.
But UI on top of a good API should be unable to cause problems; the API must prevent invalid state etc. So letting LLM’s write UI code for CRUD-apps (which is most of them…) seems fine to me.
In order to be able to enforce this hard boundary, it means that one "agent generated code" in one place, not only "can call a strictly defined set of APIs" - but, more importantly, it implements an isolated piece of functionality. Think something like: I let the AI write the dashboards, so the dashboards may be slow, crappy, incorrect, whatever - but it doesn't impact the rest of the product. However, I can't let AI write "metrics collection" because that not only impacts dashboard, but potentially impacts core product efficiency too (memory consumption, cpu, IO, etc.)
The only way things will evolve is towards maximizing AI generated code and minimizing human involvement because that is the most efficient mode for getting product to market. Any trend to the contrary is temporary, paperclip maximizer will maximize. Any code that humans touch directly will be considered implicitly too risky to run in any production environment.
We'll just have to hope things don't break too much and when they do, we'll just have to accept it because no human will be able to (or allowed to, or even trusted to) fix it.
Its just as much as an ego trip...as the Principal Engineers that roam around here, after they survived three layoffs and outputted front end code for six years straight at a FAANG...
Prove me wrong...
But oh well, who was she, just the engineer who wrote the software for the Apollo 11. Probably she had no idea what she was talking about.
/s
Not to be confused with Margaret Hamilton the actress who played the Wicked Witch of the West in the film The Wizard of Oz.
Code is not like craftsmanship anyway. The end-user doesn't see any difference whether I use microservices or hexagonal design or react or htmx or how I structure the code or which language I use. All they see is the design and the stability. Since the average developer was terrible at design anyway, the average design output from LLMs is actually an improvement in most cases, even if it always has that generic AI generated look. Stability... was not good before AI either. We have become accustomed to SAP taking 10 seconds to load each component on the page, or Atlassian spewing error messages in your face when you click submit on a ticket. It seems that the aspects that are most valued by our end users are QA and UX, not the craftsmanship of our code.
But of course, every time consumers voice their preferences is actually "a loud minority" or an "echo chamber" :)
Music is made by a human
In the same way, Suno can produce music-like audio very well. It doesn't produce music. It may be possible for somebody wielding Suno to produce music, but the average use of it certainly isn't it. It's certainly worse at producing music than almost any musician.
It can churn out muzak like no tomorrow, though, same as ChatGPT can render you barely-tolerable images of any art style you ask for.
And if you look a the dates, the rate at which they are doing it is actually increasing...
Sure but that doesnt require the person to be a carpenter though right? Anybody could do that.
It's worth remembering that, contrary to what HN acts like, software engineers are not uniformly accepting of LLMs. They are still quite controversial in the industry. Now management, on the other hand, is completely sold on the supposed benefits of LLMs. So many engineers feel forced to use them, but it's not enthusiasm on their part which drives them to that.
The twitter(x.com) and linkedin crowd has drank the cool-aid and are now swimming in it; Wild posts from FAANG engineers and managers saying they or their engineers dont read any of the code the LLM generates.
It' like saying compassion is a solved problem: just tell your problems to the LLM and have it gush out sympathy.
This has become common now, and I've seen it ruin large projects until nobody understands the codebase full of verbose nonsensical code and comments. It becomes impossible to further develop the project without AI. Contributors not using AI for any reason are all leaving because no one enjoys working with mindless robots (and humans) as colleagues. It's demeaning and devaluing their labor and humanity. They're not getting "left behind", they are leaving the madness of crowds behind.
> ..the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward *persistent dependence, with abrupt losses in cognitive competence*.
We can all see it happening. As this technology permeates society, the challenge to maintain control, coherence, and quality will become greater and ever more serious - for example, AI in healthcare, military, management and governance.
But before LLMs, it didn’t matter because on a good team you’d have a balance where maybe some PRs added complexity and some simplified it. Sometimes people would include a small related cleanup or refactor in a PR for code around what they were doing if they noticed complexity. That has become far less common, I think.
I know it’s not your point, but a 1% decrease 100 times is 0.99^100 = 0.366 which is a 63.4% decrease in understandability. Just throwing that out there
Enterprise software has always been crap.
If we don't value that though--if we don't value understanding the system and just thinking through problems as a group--then let's stop pretending and just not do code reviews. Probably nobody cares and just types "LGTM" after having their own LLM do a spot check to cover their ass.
It's really easy to push developers to stop caring. If management says "go fast fast fast, use those LLMs!", then developers can't keep up, get frustrated, stop understanding the system, and at that point they're just there to collect a paycheck before society collapses. Or, if management is making dumb decisions all the time, same thing.
Hopefully as implementing ideas takes less and less time and resources, smart people who actually care will start building things with other smart people who care.
Specifically, humans and their dysfunctions. Their fragility. Their ego tied to stuff.
This is an excellent post/reflection on the topic of solo LLM use. The only gap I see is that it assumes that one works on something alone and not in a team. Though, that might be a different essay in itself.
Edit: Turns out that footnote 3 actually mentions that already. Nice. Good post
1. Smaller PRs (that can be easily read) tend to receive more comments and nitpicks than a large change since it's easier for us to have all the PR context in our heads at the same time.
2. Most people are ego-tied to their code even if they pretend that they are impartial
3. Social dynamics around merging and reviewing code between senior/junior and older/newer employees
4. probably many more
Fighting over engineering is hard. But it is also one of the opportunities for us humans in the era of abundance.
I did learn a lot about the ecosystem and myself from this experience.
[1] https://www.nytimes.com/2018/02/16/opinion/sunday/tyranny-co...
> Once you have used a washing machine, laundering clothes by hand seems irrational, even if it might be cheaper.
Washing clothes by machine is orders of magnitude cheaper than by hand when you factor in the value of your time.
> After you have experienced streaming television, waiting to see a show at a prescribed hour seems silly, even a little undignified.
Undignified? What? Completely incoherent word slop. Good to see LLMs haven't completely taken that domain for themselves, I guess - a little hand-crafted slop now and then is a treat, I guess? Anyways, they're wrong. People still tune in to watch scheduled live events all the time.
> To resist convenience — not to own a cellphone, not to use Google
Neither of these things are a matter of convenience. A cellphone is at this point literally required to participate in a modern economy. Google is a tool that provides you with globally-retrieved information that you otherwise literally could not find on your own or without another similar tool.
I don't even disagree with the premise that convenience is a driving factor of our lives, but the justifications and conclusions drawn by this article are beyond stupid. Here's my view of it: the desire for convenience is what pushes us onwards as a species. Hunting for food is inconvenient, so we invented agriculture. Going down to the river or well for water is inconvenient, so we invented indoor plumbing. Repeat ad infinitum until we have modern civilization. There is one part the pessimistic view overlooks; that to achieve maximum convenience long-term requires more effort in the short-term. Developing the skill of programming is inconvenient in the short-term but convenient in the long-term because of the possibilities this opens up to you. This remains true even in the current LLM era, and it will remain true for as long as we are not replaced wholesale by LLMs.
I'll also note that it is less resource intensive overall. Handwashing dishes and clothes will use more water and more energy to heat that water compared to the machines that do the same job (people often assume the opposite).
It takes a mix of willpower / routine / systems / laws to keep people in check, otherwise we predictably go back to the path of least resistance.
The article is inspiring though, I have more motivation to work on my thinking process and my engineering setup (and vibe limit haha) to be more thoughtful, thank you.
1. LLM usage, in particular unsupervised, tends to build up "cognitive debt"
2. It tries to characterize the concept, including to propose a score for it
3. Notes that the level at which one can let the agents loose (and allow some CD) differs quite a lot between core business domains and more peripheral ones
I am losing more of what little focus I've had every day.
As an example, I’m juggling multiple projects at work and home. I have a finite amount of time, and access to AI. One camp would tell me to use AI for all of it to move faster, but that increases the risk of Cognitive Surrender. The other camp would tell me to do it all by hand, but I don’t have the time available for that; I must pick and choose.
So, I choose to cognitively surrender on the SEO task for our website because that’s a set of skills I don’t want to acquire and are better handled by AI anyhow. This leaves me more time to focus on learning more about Apple’s configuration profiles and improving our MDM suite, which are valuable skills that will still pay out in an AI-first world.
You have to pick and choose where to fight, otherwise you’ll expend all your energy on losses.
The economics of the whole affair unfortunately point squarely in the opposite direction.
I mean, if the management doesn't see value in decent engineering, then who am I to butt myself into the conversation?
Of course, when you're working on a system of high complexity - and one that is constantly changing to boot - with all sorts of historical reasons and what not that inform the why and so forth, you can't reasonably be expected to understand everything. This is what bracketing is for. When I acknowledge "this is the way X works", it doesn't mean I have to accept the proposition "this is the right way/best way to do X" or "X must be this way". A teacher or mentor may say it is, but in a theoretical context, unless it has been justified, you cannot claim that you know that what has been said is true. However, you can accept it as a provisional working hypothesis. And practically, it may work out and perhaps experience will lend some feeling of substance to the hypothesis, but you needn't make it doctrine.
Empirical science often has the same spirit. Perhaps the model isn't a true account of reality, but it puts handles on reality so that it can be instrumentalized.
There is an occupational danger here. If we focus exclusively on the instrumental, if we reduce all of human attitudes toward the world to use and utility, we not only vulgarize ourselves and our orientation toward the world and even others, but we risk trampling the truth and the good underfoot. We risk developing an indifference to the truth, because all that will matter is some measure of effectiveness. This is what one might call the textbook[0] definition of bullshit.
[0] https://www2.csudh.edu/ccauthen/576f12/frankfurt__harry_-_on...
Karpathy had the same trajectory. Primeagean had the same trajectory. I think in the end everyone except for Jonathan Blow will have the same trajectory
However it takes more mental energy and people don't like that
I guess you could ask the model. "Yes well done, you're a visionary software engineer".
That's very different from what a lot of vibe-coders do, like wholesale cloning and relicensing projects. This is happening in the GIS space, and these vibe-clone projects slightly hilariously inherit all the technical debt they didn't understand.
Nothing wrong with using LLMs, but I agree with Andrej Karpathy: "you can outsource your thinking, but not your understanding".
Every team I have been on have been more successful when they gave up on the product ideals and just ensured that the customers were hooked and satisfied.
Perhaps that is overrated and I should focus more on exploiting people with crappy software so that I can make more money. I guess that's what life is all about.
I care, because doing a sloppy job and seeing if people will tolerate it is not acceptable engineering practice. It's not engineering at all. More pragmatically, if you do the thing you are suggesting, you won't have users for much longer. They will abandon you and go to someone who actually makes good products.
The quality of what we provide seems to matter 0, we are up about 40% issues in prod year-to-year but the users and their engagement is also growing.
I think there is a small subset of technical nerd/idealists who think that quality is all that matters but they are in their own little sandbox while 97% of users are in another one.
This is just a derogatory comment.
It is exactly this kind of arrogance that is killing open source. In my area, GIS, there are foundational projects that are necessarily intricate. For example it might be necessary to make a four-dimensonal transform of a location, that takes into account point movement due to the Glacial Isostatic Adjustment. There are hundreds of other complex examples I could come up with.
The libraries are intricate and quality does matter.
Unfortunately the lynchpin maintainer has just gone on indefinite strike, partly because open-source vibe-coders are wholesale copying and relicensing some of these libraries in their quest to ... do what?
I actually I don't know what they want. Take all the oxygen from the room as far as I can tell.
The oxygen on the room here are peoples ego. Use your tool and interact with your community while you have their attention.
Also known as not-FAANG
> The quality of what we provide seems to matter 0
Good for you! There are lots of low skill development jobs before AI where low knowledge coders cranked out WordPress sites for marketing, advertising, and small businesses. Sounds like you got in on the ground floor!
Meanwhile companies like Amazon, one of the As in actual FAANG, do have demands on their software quality. To the extent they essentially said "knock off the tokenmaxxing productivity theater" in a recent, publicized internal memo.
Horses for courses. Sometimes you need an thoroughbred and sometimes a pack mule.
And there's nothing wrong with being a pack mule! And nobody expects a pack mule to understand the work of a thoroughbred! Nobody expects too much from a jackass after all.
Sorry for going into a tangent here, but I'm actually unsure if I hate the structures and economic incentives that makes this possible, or if I hate you specifically for defending something so rotten
Next time I rage at some broken UI in some damn service I'm forced to use because that's where people are, I'll remember this comment
The lawyers and judges who trusted an ML system to be up to snuff are currently pounding sand.
The requirements/standards for this sensor was made by this company 20 years ago. All the tests and requirements were made by this same company in a way that would minimize the costs for us to keep making new revision we could sell, and make it the hardest for any competitor to enter the market. Another competitor started with a better product abroad, so we copied it, but it didn't pass the tests, so then they installed an owner of the certification house that was a cousin of one of the VPs. No problems passing tests after that.
You think this is all controlled and nice and standardized but it's all just a market from software to hardware.
Markets also range from under regulated to over regulated, and are usually in a transition from one end to another (while contending with other forces).
I am not saying he writes all code by hand, of course. Just that he is furthest in figuring out some sort of mid point between full comprehension and cognitive surrender.
I have missed the more recent content, so perhaps I have missed something, but this was my read last I watched.