For the longest time, the joy of creation in programming came from solving hard problems. The pursuit of a challenge meant something. Now, that pursuit seems to be short-circuited by an animated being racing ahead under a different set of incentives. I see a tsunami at the beach, and I’m not sure whether I can run fast enough.
Being a nondeterministic tool, the output for a given input can vary. Rather than having a solid plan of, "if I provide this input, then that will happen", it's more like, "if I do something like this, I can expect something like that, probably, and if not, then try again until it works, I suppose".
What are the productivity gains? Obviously, it must vary. The quality of the tool output varies based on numerous criteria, including what programming language is being used and what problem is trying to be solved. The fact that person A gets a 10x productivity increase on their project does not mean that person B will also get a 10x productivity increase on their project, no matter how well they use the tool.
But again, tool usage itself is variable. Person A themselves might get a 10x boost one time, and 8x another time, and 4x another time, and 2x another time.
Man, this is giving me a cognitive dissonance compared to my experiences.
Actually, even the post itself reads like a cognitive dissonance with a dash of the usual "if it's not working for you then you are using it wrong" defence.
> There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering.
I’m convinced much of this is all noise - people seem to be focusing on the wrong unit of analysis. Producing software and lots of it has never been a problem - coming up with the right projects and producing a vertically differentiated product to what already exists is.
Does any of you bother the fact that now you have to pay money in order to do your job? I mean AI model subscriptions. Somehow it feels wrong for me to pay for tools that are trying to replace me.
IDEs used to be extremely expensive back in the 1990s. IDEs such as Microsoft Visual Studio and IBM's Visual age for Java were quite expensive subscription as I recall. subsequently, open source IDEs like Eclipse and VisualStudio seem to have become the norm.
"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind."
The "bubble" is in the financial investment, not in the technology. AI won't disappear after the bubble bursts, just like the web didn't disappear after 2000. If anything, bursting the financial bubble will most likely encourage researchers to experiment more, trying a larger range of cheaper approaches, and do more fundamental engineering rather than just scaling.
AI is here to stay, and the only thing that can stop it at this stage is a Butlerian jihad.
Borg logic consists of framing matters of choice as "inevitable". As long as those with power convince everyone that technological implementation is "inevitable", people will passively accept their self-serving and destructive technological mastery of the world.
The framing allows the rest of us to get ourselves off the hook. "We didn't have a choice! It was INEVITABLE!"
As an Opus user, I genuinely don’t understand how someone can work for weeks or months without regularly opening an IDE. The output almost always fails.
I repeatedly rewrite prompts, restate the same constraints, and write detailed acceptance criteria, yet still end up with broken or non-functional code.its very frustrating to say the least Yesterday alone I spent about $200 on generations that now require significant manual rewrites just to make them work.
At that point, the gains are questionable. My biggest success is having the model take over the first Design in my app and I take it from there, but those hundred lines if not thousand lines of code it generates are so Messi, it's insanely painful to refactor the mess afterwards
Why would you spend $200 a day on Opus if you can pay that for a month via the highest tier Claude Max subscription? Are you using the API in some special way?
This is what an AGENTS.md - https://agents.md/ (or CLAUDE.md) file is for. Put common constraints to correct model mistakes/issues with respect to the codebase, e.g. in a “code style” section.
Sometimes I have a similar file or related files. I copy their names and say use them as reference.
Code quality improves by 10 times if you do so. Even providing a a example from framework's getting started works great too for new project.
Yeah the pain of cleaning up small mess is great too. I had some tests failing and type failing issues, I thought I will fix it later by only using AI prompt. As the size was growing, failing Typescript issues was growing too. At some point it was 5000+ type issues and countless number of failing unit tests. Then more and more. I tried to fix with AI, since it was not possible fixing old way. Then I discarded the whole project when it was around 500k lines of code.
- Heavy usage of plan mode. Tell AI something like "make at least 20 searches to online documentation", support every claim with a reference, etc. Tell AI "make a task for every little thing you'll implement"
- Have the AI write tests, particularly the more expensive ones like integration and end-to-end, so you have an easy way to verify functionality.
- Setup Claude Code GHA to automatically review PRs. Give the review feedback to the agent that implemented it, either via copy-pasting or tell the agent "fetch review comments and fix them".
My trick is to explicitly roll play that we’re doing a spike. This gets all of the models to ignore all of the details they normally get hung up on. Once I have the basics in place, I can tell it to fix details.
It’s _always_ easier to add more code than it is to fix broken code.
Most people have not fully grasped how LLM's work and how to properly utilize agentic coding solutions. That is the reason for issues when it comes to vibe coders having low quality code. But that is not the limitation of technology but the user (at this stage). Basically think of it this way everyone is the grandma that has been handed a palm pilot to use to get things done. Grandma needs an iPhone not a palm pilot but the problem is that we are not in that territory yet. So now consider the people who were able to use the palm pilot very successfully and well, they were few and they were the exception, but they existed. Same here. I have been using coding agent for over 7 months now and have written zero lines of code, in fact I don't know how to code at all. But i have been able to architect very complex software projects from scratch. Text to speech , automated llm benchmarking systems for testing all possible llama.cpp sampling parameters and more, and now im building my own agentic framework from scratch. All of these things are possible and more without writing one line of code yourself. But it does require understanding how to use the technology well to get this done.
I have a hell of a time just getting any LLM to write SQL queries that have things like window functions, aggregates and lateral left joins - even when shoving the entire database schema DDL into the context.
It's so frustrating, it regularly makes me want to just quit the profession. Which is why I still just write most code by hand.
I have been telling everybody I know over the Christmas break that I have been coding from around 10-36 years of age, as a career and always in my spare time as a hobby. I have a lacklustre computer science knowledge and never worked at the scale of FANG etc but am still rather confident in my understanding of code and the tech scene in general. I've been telling people I haven't "coded" for almost 6 months now, I only interface with agentic setups and only open my IDE to make copy and config changes.
I understand we are all in different camps for a multitude of reasons;
- The jouissance of rote coding and abstraction
- The tree of knowledge specifically in programming, and which branches and nodes we each currently sit at in our understanding
- Technical paradigms that humans may have argued about have now shifted to obvious answers for agentic harnesses (think something like TDD, I for one barely used that as a style because I've mostly worked in startups building apps and found the cost of my labour not worth it, but agentic harnesse loops absolutely excel at it)
- The geography and size of the markets we work in
- The complexity of the subject matter / domain expertise
- The cost prohibitive nature of token based programming (not everyone can afford it, and the big fish seemingly have quite the advantage going fourth)
- Agentic coding has proven it can build UI's very easily, and depending on experience, it can build a very very many things easily. it excels in having feedback loops such as linting or simple javascript errors, which are observability problems in my opinion. Once it can do full stack observability (APM, system, network), it's ability to reason and correct problems on the fly for any complex system seems overly easy from my purvue.
- At the human nature level, some individuals prefer to think in 0's and 1's, some in words, some inbetween, and so on, what type of communication do agentic setups prefer?
With some of that above intuition that is easily up for debate, I've decided to lean 100% into agentic coding, I think it will be absolutely everywhere and obviously with humans in the loop but I don't think humans will need to review the pull requests. I am personally treating it as an existential threat to my career after having seen enough of what it's capable of. (with some imagination and a bit of a gambling spirit, as us mere mortals surely can't predict the future)
With my gambit, I'm not choosing to exit the tech scene and instead optimistically investing my mental prowess into figuring out where "humans in the loop" will be positioned. Currently I'm looking into CI level tooling, the known being code quality, and all the various forms of software testing paradigms. The emerging evals in my mind will keep evolving and beyond testing our ideas of model intelligence and chat bot responses will do a lot more.
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A more practical rant: If you are building a recommendation engine for A and B, the engine could have X amount of modules that return a score which when all combined make up the final decision between A and B. Forgive me but let's just use dating as an example. A product manager would say we need a new module to calculate relevance between A and B based off their food preferences. An agentic harness can easily code that module and create the tests for it. The product manager could ask an LLM to make a list of 1000 reasons why two people might be suitable for dating. The agent could easily go away and code and test all those modules and probably maintain technical consistency but drift from the companies philosophical business model. I am looking into building "semantic linting" for codebases, how can the agent maintain the code so it aligns with the company's business model. And if for whatever reason those 1000 modules need to be refactored, how can the agent maintain the code so it aligns with the company's business model. Essentially trying to mak...
interesting how can I go into building Agents? I have the kiro IDE for a project but how can I make sure what they're doing is correct? Right now i'm just vibecoding or using the more detailed requirements path but I haven't used coding Agents because I actually don't get how does the feedback loop work with them
Is there someone already mastering “agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering” ?
Why, the other rats in front of you in the race, of course!
As the pithy, if cheese expression goes, read not the times; read the eternities. People who spend so much time frantically chasing superficial ephemera like this are people without any sense of life's purpose. They're cogs in some hellish consumerist machine.
The thing that always trips me up is the lack of isolation/sandboxing that all of the AI programming tools provide.
I want to orchestrate a workforce of agents, but they can't be trusted not to run amok.
Does anyone have a better way to do this other than spinning up a cloud VM to run goose or claude or whatever poorly isolated agent tool?
What exhausts me isn’t “falling behind.” It’s watching the profession collectively decide that the solution to uncertainty is to pile abstraction on top of abstraction until no one can explain what’s actually happening anymore.
This agentic arms race by C-suite know-nothings feels less like leverage and more like denial. We took a stochastic text generator, noticed it lies confidently, wipes entire databases and harddrives, and responded by wrapping it in managers, sub-agents, memories, tools, permissions, workflows, and orchestration layers so we don’t have to look directly at the fact that it still doesn’t understand anything.
Now we’re expected to maintain a mental model not just of our system, but of a swarm of half-reliable interns talking to each other in a language that isn’t executable, reproducible, or stable.
Work now feels duller than dishwater, enough to have forced me to career pivot for 2026.
A career pivot sounds interesting. Any ideas or recommendations for others considering this? I have seen someone leave SWE to become a commercial pilot which was pretty cool.
If you want to chase the mob off the cliff, go ahead. Insanity and stupidity aren't sound life strategies, though. They're a sign you have lost the plot.
My company takes between Christmas and New Years off. I took a week before that off too. I have not used AI in that time. The slower pace of life is amazing. But when I get back to coding it will be back to running at 180%. It’s the new norm. However I’ve decided to take longer “no computer” breaks in my day. I have to adapt but I need to defend my “take it slow” times and find some analogue hobbies. The shift is real and you can’t wind it back.
I for one am not using AI, will not touch that steaming pile of manure with a 10 yard stick, and I couldn't care less about the so called magnitude 9 earthquake. When this bubble finally bursts into nothingness, I'll be still here practicing my craft and providing real value for my clients.
I am a software developer and mainly a programmer for decades now. I love programming. I love to be "once" with the computer. I will never give this joy up. If I need to sell shoes at daytime, I will program real computer programs in the evenings. If it won't be possible with modern machinery anymore, I will take my Commodore 64. I am a free man.
Wow - can we coin "Slopbrain" for people who are so far gone into AI eventualism that they can no longer function? Liked "cooked" but "slopped" or something. Good grief lol. Talk about getting lost in the sauce...
> OpenAI's sales and marketing expenses increased to _$2 billion_ in the first half of 2025.
Looks like AI companies spend enough on marketing budgets to create the illusion that AI makes development better.
Let's wait one more year, and perhaps everyone who didn't fall victim to these "slimming pills” for developers' brains will be glad about the choice they made.
129 comments
[ 3.3 ms ] story [ 109 ms ] threadWhat are the productivity gains? Obviously, it must vary. The quality of the tool output varies based on numerous criteria, including what programming language is being used and what problem is trying to be solved. The fact that person A gets a 10x productivity increase on their project does not mean that person B will also get a 10x productivity increase on their project, no matter how well they use the tool.
But again, tool usage itself is variable. Person A themselves might get a 10x boost one time, and 8x another time, and 4x another time, and 2x another time.
Actually, even the post itself reads like a cognitive dissonance with a dash of the usual "if it's not working for you then you are using it wrong" defence.
Slop-oriented programming
Using tools before their manual exists is the oldest human trick, not the newest.
AI is here to stay, and the only thing that can stop it at this stage is a Butlerian jihad.
The framing allows the rest of us to get ourselves off the hook. "We didn't have a choice! It was INEVITABLE!"
And so, we have chosen.
I repeatedly rewrite prompts, restate the same constraints, and write detailed acceptance criteria, yet still end up with broken or non-functional code.its very frustrating to say the least Yesterday alone I spent about $200 on generations that now require significant manual rewrites just to make them work.
At that point, the gains are questionable. My biggest success is having the model take over the first Design in my app and I take it from there, but those hundred lines if not thousand lines of code it generates are so Messi, it's insanely painful to refactor the mess afterwards
It’s very easy to spend $100s per dev per day.
Yeah the pain of cleaning up small mess is great too. I had some tests failing and type failing issues, I thought I will fix it later by only using AI prompt. As the size was growing, failing Typescript issues was growing too. At some point it was 5000+ type issues and countless number of failing unit tests. Then more and more. I tried to fix with AI, since it was not possible fixing old way. Then I discarded the whole project when it was around 500k lines of code.
I use Claude Code and Cursor. What I do:
- use statically typed languages: TypeScript, Go, Rust, Python w/ types
- Setup linters. For TS I have a bunch of custom lint rules (authored by AI) for common feedback that I've given. (https://github.com/shepherdjerred/monorepo/tree/main/package...)
- For Cursor, lots of feedback on my desired style. https://github.com/shepherdjerred/scout-for-lol/tree/main/.c...
- Heavy usage of plan mode. Tell AI something like "make at least 20 searches to online documentation", support every claim with a reference, etc. Tell AI "make a task for every little thing you'll implement"
- Have the AI write tests, particularly the more expensive ones like integration and end-to-end, so you have an easy way to verify functionality.
- Setup Claude Code GHA to automatically review PRs. Give the review feedback to the agent that implemented it, either via copy-pasting or tell the agent "fetch review comments and fix them".
Some examples of what I've made:
- Many features for https://scout-for-lol.com/, a League of Legends bot for Discord
- A program to generate TypeScript types for Helm charts (https://github.com/shepherdjerred/homelab/tree/main/src/helm...)
- A program to summarize all of the dependency updates for my Homelab (https://github.com/shepherdjerred/homelab/tree/main/src/deps...)
- A program to manage multiple instances of CLI agents like Claude Code (https://github.com/shepherdjerred/monorepo/tree/main/package...)
- A Discord AI bot in the style of my friends (https://github.com/shepherdjerred/monorepo/tree/main/package...)
It’s _always_ easier to add more code than it is to fix broken code.
It's so frustrating, it regularly makes me want to just quit the profession. Which is why I still just write most code by hand.
I understand we are all in different camps for a multitude of reasons;
- The jouissance of rote coding and abstraction
- The tree of knowledge specifically in programming, and which branches and nodes we each currently sit at in our understanding
- Technical paradigms that humans may have argued about have now shifted to obvious answers for agentic harnesses (think something like TDD, I for one barely used that as a style because I've mostly worked in startups building apps and found the cost of my labour not worth it, but agentic harnesse loops absolutely excel at it)
- The geography and size of the markets we work in
- The complexity of the subject matter / domain expertise
- The cost prohibitive nature of token based programming (not everyone can afford it, and the big fish seemingly have quite the advantage going fourth)
- Agentic coding has proven it can build UI's very easily, and depending on experience, it can build a very very many things easily. it excels in having feedback loops such as linting or simple javascript errors, which are observability problems in my opinion. Once it can do full stack observability (APM, system, network), it's ability to reason and correct problems on the fly for any complex system seems overly easy from my purvue.
- At the human nature level, some individuals prefer to think in 0's and 1's, some in words, some inbetween, and so on, what type of communication do agentic setups prefer?
With some of that above intuition that is easily up for debate, I've decided to lean 100% into agentic coding, I think it will be absolutely everywhere and obviously with humans in the loop but I don't think humans will need to review the pull requests. I am personally treating it as an existential threat to my career after having seen enough of what it's capable of. (with some imagination and a bit of a gambling spirit, as us mere mortals surely can't predict the future)
With my gambit, I'm not choosing to exit the tech scene and instead optimistically investing my mental prowess into figuring out where "humans in the loop" will be positioned. Currently I'm looking into CI level tooling, the known being code quality, and all the various forms of software testing paradigms. The emerging evals in my mind will keep evolving and beyond testing our ideas of model intelligence and chat bot responses will do a lot more.
---
A more practical rant: If you are building a recommendation engine for A and B, the engine could have X amount of modules that return a score which when all combined make up the final decision between A and B. Forgive me but let's just use dating as an example. A product manager would say we need a new module to calculate relevance between A and B based off their food preferences. An agentic harness can easily code that module and create the tests for it. The product manager could ask an LLM to make a list of 1000 reasons why two people might be suitable for dating. The agent could easily go away and code and test all those modules and probably maintain technical consistency but drift from the companies philosophical business model. I am looking into building "semantic linting" for codebases, how can the agent maintain the code so it aligns with the company's business model. And if for whatever reason those 1000 modules need to be refactored, how can the agent maintain the code so it aligns with the company's business model. Essentially trying to mak...
Is there someone already mastering “agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering” ?
And do they have a blog?
Why, the other rats in front of you in the race, of course!
As the pithy, if cheese expression goes, read not the times; read the eternities. People who spend so much time frantically chasing superficial ephemera like this are people without any sense of life's purpose. They're cogs in some hellish consumerist machine.
Does anyone have a better way to do this other than spinning up a cloud VM to run goose or claude or whatever poorly isolated agent tool?
This agentic arms race by C-suite know-nothings feels less like leverage and more like denial. We took a stochastic text generator, noticed it lies confidently, wipes entire databases and harddrives, and responded by wrapping it in managers, sub-agents, memories, tools, permissions, workflows, and orchestration layers so we don’t have to look directly at the fact that it still doesn’t understand anything.
Now we’re expected to maintain a mental model not just of our system, but of a swarm of half-reliable interns talking to each other in a language that isn’t executable, reproducible, or stable.
Work now feels duller than dishwater, enough to have forced me to career pivot for 2026.
Edit: Corrected since/for. :-)
Looks like AI companies spend enough on marketing budgets to create the illusion that AI makes development better.
Let's wait one more year, and perhaps everyone who didn't fall victim to these "slimming pills” for developers' brains will be glad about the choice they made.
I believe they include the costs of free ChatGPT user's in that $2B. Worth it considering the conversion rate they are getting (5-6% in Oct 2024[1]).
[1] https://www.cnet.com/tech/services-and-software/openai-cfo-p...