The "time dialation" is real. I mostly manage these days, yet my fun projects progress faster than they ever have, because I can prompt in the 2 minutes between meetings, and come back to significant progress.
I have a local LLM router app with profiles that set up the right system prompts and the right MCPs so I can swap between toolsets as I work.
This would take time to write if I’m doing it myself so I decided to vibe code it entirely. I had this idea that a compiled language is less likely to have errors (on account of the compiler giving the LLM quicker feedback than me) and so I chose Tauri with TS (I think).
The experience has been both wonderful and strange. The app was built by Claude Code with me intermittently prompting it between actual work sessions.
What’s funny is the bugs.
If you ever played Minecraft during the Alpha days you know that Notch would be like “Just fixed lighting” in one release. And you’d get that release and it’d be weird like rain would now fall through glass.
Essentially the bugs are strange. At least in the MC case you could hypothesize (transparency bit perhaps was used for multiple purposes) but this app is strange. If the LLM configuration modal is fixed, suddenly the MCP/tool tree view will stop expanding. What the heck, why are these two related? I don’t know. I could never know because I have never seen the code.
The compile time case did catch some iterations (I let Claude compile and run the program). But to be honest, the promise of correctness never landed.
Some people have been systematic and documented the prompts they use but I just free flowed it. The results are outstanding. There’s no way I could have had this built for the $50 in Claude credits. But also there’s no way I could interpret the code.
Eh, idk. First of all, the article is really wordy to say very few things. That just frustrated me a bit.
Second of all, it's easy to fart out some program in a few days vibe coding. How will that fare as more and more features need to be added on? We all used to say "Dropbox that's just FTP wrapped in a nice UI anyone can make that". This protocollie project seems to be a documentation viewer / postman for MCP. Which is cool, but is it something that would have taken a competent dev months to build? Probably not. And eventually the actual value of such things is the extensibility and integrations with various things like corporate SAML etc.
Will the vibe code projects of today be extensible like that, enough to grab market share vs the several similar versions and open source versions anyone can make in a few days, as the author suggests? It can be hard to extend a codebase you don't understand because you didn't write...
I had stumbled upon Kidlin’s Law—“If you can write down the problem clearly, you’re halfway to solving it”.
This is a powerful guiding principle in today’s AI-driven world. As natural language becomes our primary interface with technology, clearly articulating challenges not only enhances our communication but also maximizes the potential of AI.
The async approach to coding has been most fascinating, too.
I will add, I've been using Repl.it *a lot*, and it takes everything to another level. Getting to focus on problem solving, and less futzing with hosting (granted it is easy in the early journey of a product) - is an absolute game changer. Sparking joy.
I personally use the analogy of mario kart mushroom or star; that's how I feel using these tools. It's funny though, because when it goes off the rails, it really goes off the rails lol. It's also sometimes necessary to intercept decisions it will take.. babysitting can take a toll (because of the speed of execution). Having to deal with 1 stack was something.. now we're dealing with potential infinite stacks.
The challenge is that clearly stating things is and always has been the hard part. It’s awesome that we have tools which can translate clear natural language instructions into code but even if we get AGI you’ll still have to do that. Maybe you can save some time in the process by not having to fight with code as much but you’re still going to have to create really clear specs which, again, is the hard part.
I think about this a lot. Early on, as a self taught engineer, I spent a lot of time simply learning the vernacular of the software engineering world so that I could explain what it was that I wanted to do.
Because I can never focus on just one thing, I have a philosophy degree. I’ve worked with product teams and spent lots of time with stakeholders. I’ve written tons of docs because I was the only one on the team who enjoyed it.
I’ve always bemoaned my distractibility as an impediment to deep expertise, but at least it taught me to write well, for all kinds of audiences.
I've found LLMs to be a key tool in helping me articulate something clearly. I write down a few half-vague notes, maybe some hard rules, and my overall intent and ask it to articulate a spec, and then ask to for suggestions, feedback, questions to clarify from a variety of perspectives. This gives me enough material to clarify my actual requirements and then ask for that be broken down into a task list. All along the way I'm both refining my mental model and written material to more clearly communicate my intent to both machines and humans.
Increasingly I've also just ben YOLOing single shot throw-away systems to explore the design space - it is easier to refine the ideas with partially working systems than just abstract prose.
The input to output ratio is interesting. We are usually optimizing for volume of output, but now it’s inverted. I actually don’t want maximum output, I want the work split up into concrete, verifiable steps and that’s difficult to achieve consistently.
Ive taken to co-writing a plan with requirements with cursor and it works really well at first. But as it makes mistakes and we use those mistakes to refine the document eventually we are ready to “go” and suddenly it’s generating a large volume of code that directly contradicts something in the plan. Small annoyances like its inability to add an empty line after markdown headings have to be explicitly re added and re-reminded.
I almost wish I had more control over how it was iterating. Especially when it comes to quality and consistency.
When I/we can write a test and it can grind on that is when AI is at its best. It’s a closed problem. I need the tools to help me, help it, turn the open problem I’m trying to solve into a set of discrete closed problems.
I'm loving the new programming. I don't know where it goes either, but I like it for now.
I'm actually producing code right this moment, where I would normally just relax and do something else. Instead, I'm relaxing and coding.
It's great for a senior guy who has been in the business for a long time. Most of my edits nowadays are tedious. If I look at the code and decide I used the wrong pattern originally, I have to change a bunch of things to test my new idea. I can skim my code and see a bunch of things that would normally take me ages to fiddle. The fiddling is frustrating, because I feel like I know what the end result should be, but there's some minor BS in the way, which takes a few minutes each time. It used to take a whole stackoverflow search + think, recently it became a copilot hint, and now... Claude simply does it.
For instance, I wrote a mock stock exchange. It's the kind of thing you always want to have, but because the pressure is on to connect to the actual exchange, it is often a leftover task that nobody has done. Now, Claude has done it while I've been reading HN.
Now that I have that, I can implement a strategy against it. This is super tedious. I know how it works, but when I implement it, it takes me a lot of time that isn't really fulfilling. Stuff like making a typo, or forgetting to add the dependency. Not big brain stuff, but it takes time.
Now I know what you're all thinking. How does it not end up with spaghetti all over the place? Well. I actually do critique the changes. I actually do have discussions with Claude about what to do. The benefit here is he's a dev who knows where all the relevant code is. If I ask him whether there's a lock in a bad place, he finds it super fast. I guess you need experience, but I can smell when he's gone off track.
So for me, career-wise, it has come at the exact right time. A few years after I reached a level where the little things were getting tedious, a time when all the architectural elements had come together and been investigated manually.
What junior devs will do, I'm not so sure. They somehow have to jump to the top of the mountain, but the stairs are gone.
> Now that I have that, I can implement a strategy against it. This is super tedious. I know how it works, but when I implement it, it takes me a lot of time that isn't really fulfilling. Stuff like making a typo, or forgetting to add the dependency. Not big brain stuff, but it takes time.
Are people implementing stuff from start to finish in one go? For me, it's always been iterative. Start from scaffolding, get one thing right,then the next. It's like drawing. You start with a few shapes, then connect them. After you sketch on top, then do a line art, and then you finish with values (this step is also iterative refinements). With each step, you become more certain of what you want to do, while also investing the minimum possible effort.
So for me coding is more about refactoring. I always type the minimal amount of code to get something to work. And it usually means shortcuts which I annotate with a TODO comment. Then I iterate over, making it more flexible, adds more flexibility, makes the code more clean.
Have you had the realization that you could never go back to dealing with all the minutia again?
LLMs have changed me. I want to go outside while they are working and I am jealous of all the young engineers that won’t lose the years I did sitting in front of a screen for 12 hours a day while sometimes making no progress on connecting two black boxes.
> So for me, career-wise, it has come at the exact right time. A few years after I reached a level where the little things were getting tedious, a time when all the architectural elements had come together and been investigated manually.
Wish I had your confidence in this. I can easily see how this nullifies my hard earned experience and basically puts me in the same sport as a more mid level or even junior engineer.
Right, I’ve been using it recently for writing a message queue -> database bridge with checkpointing and all kinds of stuff (I work for a timeseries database company).
I saw this as a chance to embrace AI, after a while of exploring I found Claude Code, and ended up with a pretty solid workflow.
But I say this as someone who has worked with distributed systems / data engineering for almost 2 decades, and spend most of my time reviewing PRs and writing specs anyway.
The trick is to embrace AI on all levels: learn how to use prompts. learn how to use system prompts. learn how to use AI to optimize these prompts. learn how to first write a spec, and use a second AI (“adversarial critic”) to poke holes in that plan. find incompletenesses. delegate the implementation to a cheaper model. learn how to teach AI how to debug problems properly, rather than trying to one-shot fixes in the hope it fixes things. etc
It’s an entirely different way of working.
I think juniors can learn this as well, but need to work within very well-defined frameworks and probably needs to be part of college curriculum as well.
At one hand you get insane productivity boost, something that could take maybe days, weeks or months to do now you can do in significantly shorter amount of time, but how much are you learning if you are at a junior level and not consciously being careful about how you use it, feels like it can be dangerous without a critical mindset, where you eventually rely too much on it that you can't survive without it. Or maybe this is ok? Perhaps the way of programming in the future should be like this, since we have this technology now, why not use it?
Like there's a mindset where you just want to get the job done, ok cool just let the llm do it for me (and it's not perfect atm), and ill stitch everything together fix small stuff that it gets wrong etc, saves alot of time and sure I might learn something in the process as well.
And then the other way of working is the traditional way, you google, look up on stackoverflow, read documentations, you sit down try to find out what you need and understand the problem, code a solution iteratively and eventually you get it right and you get a learning experience out of it. Downside is this can take 100 years, at the very least much longer than using an llm in general. And you could argue that if you prompt the llm in a certain way, it would be equivalent to doing all of this but in a faster way, without taking away from you learning.
For seniors it might be another story, it's like they have the critical thinking, experience and creativity already, through years of training, so they don't loose as much compared to a junior. It will be closer for them to treat this as a smarter tool than google.
Personally, I look at it like you now have a smarter tool, a very different one as well, if you use it wisely you can definitely do better than traditional googling and stackoverflow. It will depend on what you are after, and you should be able to adapt to that need. If you just want the job done, then who cares, let the llm do it, if you want to learn you can prompt it in certain way to achieve that, so it shouldn't be a problem. But this sort of way of working requires a conscious effort on how you are using it and an awareness of what downsides there could be if you choose to work with the llm in a certain way to be able to change the way you interact with the llm. In reality I think most people don't go through the hoops of "limiting" the llm so that you can get a better learning experience. But also, what is a better learning experience? Perhaps you could argue that being able to see the solution, or a draft of it, can be a way of speeding up learning experience, because you have a quicker starting point to build upon a solution. I dunno. My only gripe with using LLM, is that deep thinking and creativity can take a dip, you know back in the day when you stumbled upon a really difficult problem, and you had to sit down with it for hours, days, weeks, months until you could solve that. I feel like there are some steps there that are important to internalize, that LLM nowdays makes you skip.
What also would be so interesting to me is to compare a senior that got their training prior to LLM, and then compare them to a senior now that gets their training in the new era of programming with AI, and see what kinds of differences one might find
I would guess that the senior prior to LLM era, would be way better at coding by hand in general, but critical thinking and creativity, given that they both are good seniors, maybe shouldn't be too different honestly
but it just depends on how that other senior, who are used to working with LLMs, interacts with them.
Also I don't like how LLM sometimes can influence your approach to solving something, like perhaps you would have thought about a better way or different way of solving a problem if you didn't first ask the LLM. I think this could be true to a higher degree for juniors than seniors due to gap in experience
when you are senior, you sort of have seen alot of things already, so...
My theory on AI is it's the next iteration of google search, a better more conversational, base layer over all the information that exists on the internet.
Of course some people will lose jobs just like what happened to several industries when search became ubiquitous. (newspapers, phone books, encyclopedias, travel agents)
But IMHO this isn't the existential crisis people think it is.
It's just a tool. Smart, clever people can do lots of cool stuff with tools.
But you still have to use it,
Search has just become Chat.
You used to have to search, now you chat and it does the searching, and more!
Yeah; there's still a massive chasm between "I spent hours precisely defining my requirements for this greenfield application with no users and the AI one-shot it" and "million line twenty team enterprise SaaS hellscale with ninety-seven stakeholders per line of code".
The fact that AI can actually handle the former case is, to be clear, awesome; but not surprising. Low-code tools have been doing it for years. Retool, even back in 2018, was way more productive than any LLMs I've seen today, at the things Retool could do. But its relative skill at these things, to me, does not conclusively determine that it is on the path toward being able to autonomously handle the latter.
The english language is simply a less formal programming language. Its informality means it requires less skill to master, but also means it may require more volume to achieve desired outcomes. At some level of granularity, it is necessarily the case that programming in english begins to look like programming in javascript; just with capital letters, exclamation points, and threats to fire the AI instead of asserts and conditionals. Are we really saving time, and thus generating higher levels of productivity? Or, is its true benefit that it enables foray into languages and domains you might be unfamiliar with; unlocking software development for a wider range of people who couldn't muster it before? Its probably a bit of both.
Dario Amodei says we'll have the first billion dollar solo-company by 2026 [1]. I lean toward this not happening. I would put money on even $100M not happening, barring some level of hyperinflation which changes our established understanding of what a dollar even is. But, here's what I will say: hitting levels of revenue like this, with a human count so low that the input of the AI has to overwhelm the input from the humans, is the only way to prove to me that, actually, these things might be more than freakin awesome tools. Blog posts from people making greenfield apps named after a furrsona DJ isn't moving the needle for me on this issue.
AI is still in an experimental phase for many teams, especially when it comes to handling complex, long-term projects. For PMs and EMs, the cost-benefit analysis of AI credits vs. manual tasks is a big concern before fully committing to AI adoption. Some teams have seen great success, particularly in areas where speed and flexibility are key, but others are still waiting for clearer ROI before diving in. It’ll be interesting to see how the balance of risk and reward evolves as AI tools mature.
Im going to be overly picky about the subheading (which is an incidental aspect of TFA): “The future of software development might just be jazz. Everyone improvising. Nobody following the sheet music.”
That’s not jazz. Jazz being what it is, a lot of people in 2025 think it’s “everyone improvising,” but (outside of some free jazz) it’s quite structured and full of shared conventions.
Analogies work when you and your audience both understand the things being compared. In this case, the author doesn’t, and maybe some of the audience shares the same misperception, and so the analogy only works based on shared misunderstanding.
The analogy to jazz actually works better the more you know about it. But that’s accidental.
Software jazz is when you and your teammate are working hard on a project and you both land big PRs at the same time without a merge conflict, then integrate before the build system even has time to realize what happened.
Reading articles like this feels like being in a different reality.
I don't work like this, I don't want to work like this and maybe most importantly I don't want to work with somebody who works like this.
Also I am scared that any library that I am using through the myriad of dependencies is written like this.
On the other hand... if I look at this as some alternate universe where I don't need to directly or indirectly touch any of this... I am happy that it works for these people? I guess? Just keep it away from me
I tend to generally think the same as you, as I work in the same field. A long time ago I thought to myself, if AI adoption increases exponentially, there is a chance that the amount of security vulnerabilities introduced by it also increases at the same rate.
However, what we are maybe not considering enough is that general AI adoption could and almost certainly will affect the standards for cybersecurity as well. If everyone uses AI and everyone gets used to its quirks and mistakes and is also forgiving about someone else using it since they themselves use it too, the standards for robust and secure systems could decrease to adjust to that. Now, your services as a cybersecurity consultant are no longer in need as much, as whatever company would need them can easily point to all the other companies also caring less and not doing anything about the security issues introduced by the AI that everyone uses. The legal/regulation body would also have to adjust to this, as it is not possible to enforce certain standards if no one can adhere to them.
Not sure what the complaint is about. If the coding work has to be thrown away, we need to do that and move on. We did that many times earlier. We have thrown away hunting, farming, calculations by hand, cameras and so on. Coding work might get extinct for some use cases. Nothing wrong with it. Learn how to use your tools, assistants and godzillas.
The bigger issue, would there be a need for coding and software? Who would use them? Why are they using it? Are they buying something? searching for info? The usecase will see a revolution. The new usecases won't need the traditonal kind software. But AI can only produce traditional software.
Can I ask Claude to code up its clone for local use?
It’s amazing to me all the Luddite developers who are “against” all this.
Completely new ways of programming are forming, completely new ways of computing and the best the luddites can do is be “against it”.
A revolution came along, a change in history and instead of being excited by the possibilities, joining in, learning, discovering, creating …… the luddites are just “against it all”.
I feel sorry for them. Why be in computing at all if you don’t like new technology?
"You’re using it wrong" arguments/hype articles showing up. Speculators love it. But in reality if you need to extol the benefits of AI, then is it really the user or the technology?
Honestly reminds me of the digital currency mania that busted a couple of years ago. Same types of articles popping up too.
Look I understand the benefits of AI but it’s clear ai is limited by the compute power of today. Maybe the dream this author has will be realized some day. But it won’t be today or in current generations lifespan.
>I've been coding for long enough to remember when we carved HTML tables by hand. When CSS was a suggestion, not a lifestyle. When JavaScript was for mouseover effects and nothing else.
Cringe. The tech is half baked and the author is already fully committed to this is the future, I am living in the future, I bake cookies while Claude codes.
Pure cringe. This confirms my earlier theories that everyone just wants to be a manager. You don't need to manage humans. You just want to be a manager.
The whole article could be summed down to I always wanted to be a manager and now I am a manager of bots.
Based on how easy it is to trigger cool-down or throttle on Claude Code, I think people know how to build with AI. Or they’re trying really hard to figure it out. The race is on and it’s wide open.
There are a lot gotchas with these new models. They get incredibly lazy if you let them. For example, I asked it to do a simple tally by year. I just assumed it’s simple enough I don’t need to ask to write a code. It counted first couple of years and just “guessed” the rest based on pattern it noticed.
Sometimes, it feels like having a lazy coworker that you have to double check constantly and email with repeated details. Other times, I just sit there in awe of how smart it is in my weekly AGI moment and how it’s going to replace me soon.
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[ 2.8 ms ] story [ 104 ms ] threadThis would take time to write if I’m doing it myself so I decided to vibe code it entirely. I had this idea that a compiled language is less likely to have errors (on account of the compiler giving the LLM quicker feedback than me) and so I chose Tauri with TS (I think).
The experience has been both wonderful and strange. The app was built by Claude Code with me intermittently prompting it between actual work sessions.
What’s funny is the bugs. If you ever played Minecraft during the Alpha days you know that Notch would be like “Just fixed lighting” in one release. And you’d get that release and it’d be weird like rain would now fall through glass.
Essentially the bugs are strange. At least in the MC case you could hypothesize (transparency bit perhaps was used for multiple purposes) but this app is strange. If the LLM configuration modal is fixed, suddenly the MCP/tool tree view will stop expanding. What the heck, why are these two related? I don’t know. I could never know because I have never seen the code.
The compile time case did catch some iterations (I let Claude compile and run the program). But to be honest, the promise of correctness never landed.
Some people have been systematic and documented the prompts they use but I just free flowed it. The results are outstanding. There’s no way I could have had this built for the $50 in Claude credits. But also there’s no way I could interpret the code.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
Developers believe they complete tasks 25% faster with AI but when measured they are 19% slower when using AI.
Second of all, it's easy to fart out some program in a few days vibe coding. How will that fare as more and more features need to be added on? We all used to say "Dropbox that's just FTP wrapped in a nice UI anyone can make that". This protocollie project seems to be a documentation viewer / postman for MCP. Which is cool, but is it something that would have taken a competent dev months to build? Probably not. And eventually the actual value of such things is the extensibility and integrations with various things like corporate SAML etc.
Will the vibe code projects of today be extensible like that, enough to grab market share vs the several similar versions and open source versions anyone can make in a few days, as the author suggests? It can be hard to extend a codebase you don't understand because you didn't write...
I had stumbled upon Kidlin’s Law—“If you can write down the problem clearly, you’re halfway to solving it”.
This is a powerful guiding principle in today’s AI-driven world. As natural language becomes our primary interface with technology, clearly articulating challenges not only enhances our communication but also maximizes the potential of AI.
The async approach to coding has been most fascinating, too.
I will add, I've been using Repl.it *a lot*, and it takes everything to another level. Getting to focus on problem solving, and less futzing with hosting (granted it is easy in the early journey of a product) - is an absolute game changer. Sparking joy.
I personally use the analogy of mario kart mushroom or star; that's how I feel using these tools. It's funny though, because when it goes off the rails, it really goes off the rails lol. It's also sometimes necessary to intercept decisions it will take.. babysitting can take a toll (because of the speed of execution). Having to deal with 1 stack was something.. now we're dealing with potential infinite stacks.
I’ve always bemoaned my distractibility as an impediment to deep expertise, but at least it taught me to write well, for all kinds of audiences.
Boy do I feel lucky now.
Increasingly I've also just ben YOLOing single shot throw-away systems to explore the design space - it is easier to refine the ideas with partially working systems than just abstract prose.
Ive taken to co-writing a plan with requirements with cursor and it works really well at first. But as it makes mistakes and we use those mistakes to refine the document eventually we are ready to “go” and suddenly it’s generating a large volume of code that directly contradicts something in the plan. Small annoyances like its inability to add an empty line after markdown headings have to be explicitly re added and re-reminded.
I almost wish I had more control over how it was iterating. Especially when it comes to quality and consistency.
When I/we can write a test and it can grind on that is when AI is at its best. It’s a closed problem. I need the tools to help me, help it, turn the open problem I’m trying to solve into a set of discrete closed problems.
I'm actually producing code right this moment, where I would normally just relax and do something else. Instead, I'm relaxing and coding.
It's great for a senior guy who has been in the business for a long time. Most of my edits nowadays are tedious. If I look at the code and decide I used the wrong pattern originally, I have to change a bunch of things to test my new idea. I can skim my code and see a bunch of things that would normally take me ages to fiddle. The fiddling is frustrating, because I feel like I know what the end result should be, but there's some minor BS in the way, which takes a few minutes each time. It used to take a whole stackoverflow search + think, recently it became a copilot hint, and now... Claude simply does it.
For instance, I wrote a mock stock exchange. It's the kind of thing you always want to have, but because the pressure is on to connect to the actual exchange, it is often a leftover task that nobody has done. Now, Claude has done it while I've been reading HN.
Now that I have that, I can implement a strategy against it. This is super tedious. I know how it works, but when I implement it, it takes me a lot of time that isn't really fulfilling. Stuff like making a typo, or forgetting to add the dependency. Not big brain stuff, but it takes time.
Now I know what you're all thinking. How does it not end up with spaghetti all over the place? Well. I actually do critique the changes. I actually do have discussions with Claude about what to do. The benefit here is he's a dev who knows where all the relevant code is. If I ask him whether there's a lock in a bad place, he finds it super fast. I guess you need experience, but I can smell when he's gone off track.
So for me, career-wise, it has come at the exact right time. A few years after I reached a level where the little things were getting tedious, a time when all the architectural elements had come together and been investigated manually.
What junior devs will do, I'm not so sure. They somehow have to jump to the top of the mountain, but the stairs are gone.
"That's OK, I found a jetpack."
Many times adding a new junior to a team makes it slower.
How does using llms as junior makes you more productive?
Are people implementing stuff from start to finish in one go? For me, it's always been iterative. Start from scaffolding, get one thing right,then the next. It's like drawing. You start with a few shapes, then connect them. After you sketch on top, then do a line art, and then you finish with values (this step is also iterative refinements). With each step, you become more certain of what you want to do, while also investing the minimum possible effort.
So for me coding is more about refactoring. I always type the minimal amount of code to get something to work. And it usually means shortcuts which I annotate with a TODO comment. Then I iterate over, making it more flexible, adds more flexibility, makes the code more clean.
LLMs have changed me. I want to go outside while they are working and I am jealous of all the young engineers that won’t lose the years I did sitting in front of a screen for 12 hours a day while sometimes making no progress on connecting two black boxes.
Wish I had your confidence in this. I can easily see how this nullifies my hard earned experience and basically puts me in the same sport as a more mid level or even junior engineer.
I see it as a worrying extension of a pre-LLM problem: No employer wants to train, they just want to hire employees after someone else trains them.
I saw this as a chance to embrace AI, after a while of exploring I found Claude Code, and ended up with a pretty solid workflow.
But I say this as someone who has worked with distributed systems / data engineering for almost 2 decades, and spend most of my time reviewing PRs and writing specs anyway.
The trick is to embrace AI on all levels: learn how to use prompts. learn how to use system prompts. learn how to use AI to optimize these prompts. learn how to first write a spec, and use a second AI (“adversarial critic”) to poke holes in that plan. find incompletenesses. delegate the implementation to a cheaper model. learn how to teach AI how to debug problems properly, rather than trying to one-shot fixes in the hope it fixes things. etc
It’s an entirely different way of working.
I think juniors can learn this as well, but need to work within very well-defined frameworks and probably needs to be part of college curriculum as well.
Like there's a mindset where you just want to get the job done, ok cool just let the llm do it for me (and it's not perfect atm), and ill stitch everything together fix small stuff that it gets wrong etc, saves alot of time and sure I might learn something in the process as well. And then the other way of working is the traditional way, you google, look up on stackoverflow, read documentations, you sit down try to find out what you need and understand the problem, code a solution iteratively and eventually you get it right and you get a learning experience out of it. Downside is this can take 100 years, at the very least much longer than using an llm in general. And you could argue that if you prompt the llm in a certain way, it would be equivalent to doing all of this but in a faster way, without taking away from you learning.
For seniors it might be another story, it's like they have the critical thinking, experience and creativity already, through years of training, so they don't loose as much compared to a junior. It will be closer for them to treat this as a smarter tool than google.
Personally, I look at it like you now have a smarter tool, a very different one as well, if you use it wisely you can definitely do better than traditional googling and stackoverflow. It will depend on what you are after, and you should be able to adapt to that need. If you just want the job done, then who cares, let the llm do it, if you want to learn you can prompt it in certain way to achieve that, so it shouldn't be a problem. But this sort of way of working requires a conscious effort on how you are using it and an awareness of what downsides there could be if you choose to work with the llm in a certain way to be able to change the way you interact with the llm. In reality I think most people don't go through the hoops of "limiting" the llm so that you can get a better learning experience. But also, what is a better learning experience? Perhaps you could argue that being able to see the solution, or a draft of it, can be a way of speeding up learning experience, because you have a quicker starting point to build upon a solution. I dunno. My only gripe with using LLM, is that deep thinking and creativity can take a dip, you know back in the day when you stumbled upon a really difficult problem, and you had to sit down with it for hours, days, weeks, months until you could solve that. I feel like there are some steps there that are important to internalize, that LLM nowdays makes you skip. What also would be so interesting to me is to compare a senior that got their training prior to LLM, and then compare them to a senior now that gets their training in the new era of programming with AI, and see what kinds of differences one might find I would guess that the senior prior to LLM era, would be way better at coding by hand in general, but critical thinking and creativity, given that they both are good seniors, maybe shouldn't be too different honestly but it just depends on how that other senior, who are used to working with LLMs, interacts with them.
Also I don't like how LLM sometimes can influence your approach to solving something, like perhaps you would have thought about a better way or different way of solving a problem if you didn't first ask the LLM. I think this could be true to a higher degree for juniors than seniors due to gap in experience when you are senior, you sort of have seen alot of things already, so...
Why are these chatbots that mangle data 1/3 to 1/2 of the time getting their budgets 10x over and over again?
This is irrational. If the code mangles data this bad, it's garbage.
Of course some people will lose jobs just like what happened to several industries when search became ubiquitous. (newspapers, phone books, encyclopedias, travel agents)
But IMHO this isn't the existential crisis people think it is.
It's just a tool. Smart, clever people can do lots of cool stuff with tools.
But you still have to use it,
Search has just become Chat.
You used to have to search, now you chat and it does the searching, and more!
The fact that AI can actually handle the former case is, to be clear, awesome; but not surprising. Low-code tools have been doing it for years. Retool, even back in 2018, was way more productive than any LLMs I've seen today, at the things Retool could do. But its relative skill at these things, to me, does not conclusively determine that it is on the path toward being able to autonomously handle the latter.
The english language is simply a less formal programming language. Its informality means it requires less skill to master, but also means it may require more volume to achieve desired outcomes. At some level of granularity, it is necessarily the case that programming in english begins to look like programming in javascript; just with capital letters, exclamation points, and threats to fire the AI instead of asserts and conditionals. Are we really saving time, and thus generating higher levels of productivity? Or, is its true benefit that it enables foray into languages and domains you might be unfamiliar with; unlocking software development for a wider range of people who couldn't muster it before? Its probably a bit of both.
Dario Amodei says we'll have the first billion dollar solo-company by 2026 [1]. I lean toward this not happening. I would put money on even $100M not happening, barring some level of hyperinflation which changes our established understanding of what a dollar even is. But, here's what I will say: hitting levels of revenue like this, with a human count so low that the input of the AI has to overwhelm the input from the humans, is the only way to prove to me that, actually, these things might be more than freakin awesome tools. Blog posts from people making greenfield apps named after a furrsona DJ isn't moving the needle for me on this issue.
[1] https://www.inc.com/ben-sherry/anthropic-ceo-dario-amodei-pr...
That’s not jazz. Jazz being what it is, a lot of people in 2025 think it’s “everyone improvising,” but (outside of some free jazz) it’s quite structured and full of shared conventions.
Analogies work when you and your audience both understand the things being compared. In this case, the author doesn’t, and maybe some of the audience shares the same misperception, and so the analogy only works based on shared misunderstanding.
The analogy to jazz actually works better the more you know about it. But that’s accidental.
I don't work like this, I don't want to work like this and maybe most importantly I don't want to work with somebody who works like this.
Also I am scared that any library that I am using through the myriad of dependencies is written like this.
On the other hand... if I look at this as some alternate universe where I don't need to directly or indirectly touch any of this... I am happy that it works for these people? I guess? Just keep it away from me
And also to help me troubleshoot my old yacht, it taught me to be an amateur marine electrician
I do not let it into my entire codebase tho. Keep the context small and if I dont get what I want in one or two prompt I dont use it
Man, I'm going to make so much money as a Cybersecurity Consultant!
However, what we are maybe not considering enough is that general AI adoption could and almost certainly will affect the standards for cybersecurity as well. If everyone uses AI and everyone gets used to its quirks and mistakes and is also forgiving about someone else using it since they themselves use it too, the standards for robust and secure systems could decrease to adjust to that. Now, your services as a cybersecurity consultant are no longer in need as much, as whatever company would need them can easily point to all the other companies also caring less and not doing anything about the security issues introduced by the AI that everyone uses. The legal/regulation body would also have to adjust to this, as it is not possible to enforce certain standards if no one can adhere to them.
The bigger issue, would there be a need for coding and software? Who would use them? Why are they using it? Are they buying something? searching for info? The usecase will see a revolution. The new usecases won't need the traditonal kind software. But AI can only produce traditional software.
Can I ask Claude to code up its clone for local use?
Really helped my understanding of how apps work.
Completely new ways of programming are forming, completely new ways of computing and the best the luddites can do is be “against it”.
A revolution came along, a change in history and instead of being excited by the possibilities, joining in, learning, discovering, creating …… the luddites are just “against it all”.
I feel sorry for them. Why be in computing at all if you don’t like new technology?
I call it 'Orchestratic Development'.
Edit: Seriously, down voted twice when just commenting on an article? God I hate this arrogant shithole.
Honestly reminds me of the digital currency mania that busted a couple of years ago. Same types of articles popping up too.
Look I understand the benefits of AI but it’s clear ai is limited by the compute power of today. Maybe the dream this author has will be realized some day. But it won’t be today or in current generations lifespan.
Cringe. The tech is half baked and the author is already fully committed to this is the future, I am living in the future, I bake cookies while Claude codes.
Pure cringe. This confirms my earlier theories that everyone just wants to be a manager. You don't need to manage humans. You just want to be a manager.
The whole article could be summed down to I always wanted to be a manager and now I am a manager of bots.
There are a lot gotchas with these new models. They get incredibly lazy if you let them. For example, I asked it to do a simple tally by year. I just assumed it’s simple enough I don’t need to ask to write a code. It counted first couple of years and just “guessed” the rest based on pattern it noticed.
Sometimes, it feels like having a lazy coworker that you have to double check constantly and email with repeated details. Other times, I just sit there in awe of how smart it is in my weekly AGI moment and how it’s going to replace me soon.