This is legitimately the reason I'm looking to leave programming.
I got into programming because the problems of programming were interesting to me. But if the problems go from "figure out why this calculator is off by one in France" to "Get this LLM to stop spamming cutsey emojis", then maybe it's time for a career change.
I do not have the burnout but I certainly operate similarly to the author. I continue to be unable to establish a workflow where allowing the LLM to generate code that I review is faster than writing the code myself. Literally the only two ways out of this dilemma is to blindly trust what was generated or to generate an uncharacteristically exhaustive suite of unit tests to validate every possible scenario. I just write the business logic myself and have the LLM do a lot of the rest. Boilerplate falls into the latter as well.
I don’t understand what could possibly need to be made so fast that isn’t totally made up billable hours. Running at top speed long enough to be burned out is either ineffective, or valuable enough that someone else can take over while you sleep.
It sounds kind of like being stuck working with coworkers who--while not overtly hostile--need constant hand-holding and repeat the same kinds of mistakes every day and can't even be genuinely sorry about it.
Just because we work with computers doesn't mean we don't take, er, social-damage. Or perhaps parasocial damage, in this case.
I've taken a bit longer than I wanted but it will be open sourced soon.
It's a durable orchestration engine that takes in specs/requirements and coordinates agents externally (meaning the engine drives the loop, not an agent) until the work is fully implemented/verified and reviewed.
It's meant to be used with any harness as basically the last step. You plan your work with whatever LLM you use and then hand off implementation to the engine (through an MCP server or other surfaces)
It can use your OpenAI/Anthropic subscriptions or any other provider and you can mix and match models across implementation and review in any way you want with fan out for parallel reviewers and more.
The goal is to produce high quality unsupervised code that matches your requirements and is reviewed throughout the implementation rather than at the end only, so that mistakes don't compound.
I think having style guidance in your context is valuable for avoiding this kind of thing. Having to read awful, cliched text all day is even worse than having to read reams of useless code. I have some simple humanizing content in there that specifically calls out the rhetorical devices that AI loves, and it drastically improves the diffs and comments. It also makes the coding performance generally slightly worse, but ergonomics uber alles.
I don't think I have a "burnout", but LLMs are really exhausting due to amount of pressure they generate. No one is really pushing me to increase my workload, but at every moment there is always something ready, done by my clankers or clankers of other people that I could be unblocking. In the past (before LLMs) it was already hard to keep up, but now it feels like there's 10x more things waiting at any given time, and there could be 10x more if everyone just "optimized" and streamlined processes fed the AI even more tasks in parallel faster. It just being a bottleneck of everything, all the time is tiring...
I am happy about all the little side-projects, and ideas it help my realize, and I enjoy exploring this new world, but I've noticed LLMs feed my unhealthy "don't want to take a break and waste time being idle" mindset, and I need to correct it.
W.r.t. article's main complain - I think the similar thing happened due to factory manufacturing automation. What used to be a varied skillful craft in a shop became standing in a single place of an assembly line doing the exact same thing whole day. LLM took away the more creative and variable part of the work, and left the repetitive QA rubber-stamping. Probably some of the mitigations used back then could be rediscovered today.
> I don't think I have a "burnout", but LLMs are really exhausting due to amount of pressure they generate. No one is really pushing me to increase my workload, but at every moment there is always something ready, done by my clankers or clankers of other people that I could be unblocking.
I see a different type of pressure: I'm at a company that still is requiring everyone use LLMs with token leaderboards, time-spent measurements, and impacts to performance reviews, and all that. So I find myself having to carve out some percent of my time to stop doing productive work, and "go do AI to show token use." So my workload hasn't changed (or it's gone up), but I have N% less time to work on it because I have to spend time appeasing the AI gods...
I am happy about all the little side-projects, and ideas it help my realize..
Same, but I really have to fight the urge to just add fun new features to things I work on any time inspiration strikes. I am an appalling 'feature factory' if I don't actively keep myself in check. The cost of just building everything is so low, but the value of those things is also incredibly low, so I'm often just bloating what I build.
There's been a lot of articles and posts about the increasing importance of 'taste' in software built with AI, and I'm finding I know need to look for strategies to find some.
I echo this entirely, brother. I think a lot of us developers have a lot of ideas that were unrealized, and now we have this opportunity to do it. And any time an LLM is sitting idle, it feels like we're wasting our time. Why aren't we having it built something for us? Currently, I work on about three projects at work at the same time and about four personal projects at the same time. My day just zips by. I'll burn four hours without even thinking about it. It's exhausting but exhilarating. I do wonder if burnouts in the future though.
> I think the similar thing happened due to factory manufacturing automation. What used to be a varied skillful craft in a shop became standing in a single place of an assembly line doing the exact same thing whole day.
I had to think of the factory scenes in Charlie Chaplin's Modern Times. The author's feeling is basically the main idea of the sketches, i.e. humans having to follow the pace of the machines instead of the other way around.
Reverse centaurs are nothing new. Ask any worker movement from the last centuries.
> LLM took away the more creative and variable part of the work, and left the repetitive QA rubber-stamping
“I wanted a machine to do the dishes so I could concentrate on my creative work, and all I got was a machine to do my work so I’m left to wash the dishes.”
I wonder if anybody has an implicit fear that with LLM you're expected to be a 20x engineering all the time, otherwise you're out. Can also lead to people producing shiny apps that impress others (sometimes for legit reasons) even though they have no idea how anything work. A "ship value" culture will not bother with the inner workings or actual skills.
> I've noticed LLMs feed my unhealthy "don't want to take a break and waste time being idle" mindset, and I need to correct it.
Embrace it if you’re like me and feel uncomfortable having an idle mind, embrace it! You’ll get more done and being 120% go go go is impossible over the long run so eventually your body will just say I need a break then once you recover full steam head again on the treadmill
> but at every moment there is always something ready
Yes, this is to me the primary driver of the extreme AI burnout. In ~30 years in Silicon Valley and many, many startups, the pressure has never been as intense.
Before AI I'd mostly work on one thing at a time (at least within a given hour) and in the evening I wouldn't start a new 6 hour task because it's too long, so tomorrow is another day.
Now, that 6 hour task is more like 30 minutes, so there is intense pressure to just knock it off tonight. And then the next one. And one more. And while the bot is thinking, to have 4 other work streams in parallel so there is never, ever, a break in the day. The human mind is not built for 100% utilization 15 hours a day.
> In the past (before LLMs) it was already hard to keep up, but now it feels like there's 10x more things waiting at any given time, and there could be 10x more if everyone just "optimized" and streamlined processes fed the AI even more tasks in parallel faster.
I find LLMs to help me manage the unrealistic workload I have, because at least now it's feasible instead of just getting more work piled on top of me with a never ending backlog (that people actually expect me to thin, not let grow). Add on top of that colleagues that would have death by commitee'd many ideas and now just have to argue against actual MVPs that work instead of ideas (or can be proven to not work and discarded without wasting time on them in some cases), and I don't even hate my job as much!
It's just that to ensure that the technology is not a net negative, I need millions upon millions of tokens every single day (tool runs, adversarial reviews, testing), but once you get that inflection point, alongside needing a good enough model, the floor for which currently I'd say GLM 5.2 on Max reasoning reaches, or use something like SOTA Anthropic/OpenAI models, it becomes a pretty good way of working. That said if you have missing pieces there (e.g. using cheap models that aren't very good), the curve of getting stuff done can go downwards and you'll just end up with a lot of slop - useless docs, bad code and an ever increasing amount of technical debt.
On average, each task that I do, needs about 15 minutes to 2 hours of planning and making the agents explore the codebase and refine the plans first.
Curiously, in my case this leads to less burnout cause I can actually pause and grab a drink, meal or go for a walk, while parallel agents do the work, once I've planned things well enough and have dispatched something that will work for 1-4 hours. I don't have to review their output immediately once they finish but can just batch things.
Hmm. When you put it that way, it sounds like LLMs and social media trigger the same "I have to see what's going on now" pattern (and therefore can wind up at the same kind of addiction, with the same problems).
I don't have much success with using the LLM to make changes to a big legacy codebase. Instead, I use the LLM to gripe about things I don't like in the code. Usually, it is a brilliant commiserator.
I've started feeling slightly physically ill when I read Opus output for hours straight. This article rings very true for me. I've started complaining about it with my team; at least have a personal style guide in your agent rules that eliminates emdashes, the "it's not X, it's Y"s, the long lists of modifiers before the noun, using the word "land" to mean finish, etc. I hope this is just a phase of adolescent LLMs.
Me too. It feels like I’m taking psychic damage from reading so much of this stuff. Contrary to the theory that it’s “just the contract workers’ Nigerian English,” I think the models are developing an ultra-terse hyper-stylized dialect of their own under RL pressure. They seem to be writing increasingly in _code_, and I don’t mean computer code. The words don’t mean quite what they mean to humans.
"That's such a clever way to see things! Let's delve into that!"
The bots (all of them) seem to show patterns of overuse of specific phrases, words, and punctuation.
Some of those are the ones you mentioned. Another that I've been seeing lately is overuse of the term "gate", wherein: As a human, I know what a gate is. A gate is a thing that can be open, or that can be closed. It might be locked or unlocked. The path beyond the gate may be passable or impassable or nonexistent. The gate is just a gate, and the presence of the gate doesn't imply whether it is open or closed.
But in bot-speak, a gate only refers to a hard block -- an impassable construct. Like a fence or a wall, or even a lava-filled moat.
But while a lava-filled moat is intended to be impassable, the bot uses "gate" -- a thing that is designed to be passed -- to describe that same kind of obstacle.
That's misuse of the term, I think, based on decades of dealing with gates in reality: Usually when I encounter a gate that is closed, I just open it and walk through.
I do have instructions that tell the bot to avoid that usage of the word and it ignores them sometimes anyway.
But "gate" is just today's problem-word that comes to mind as I write this. Yesterday, it was something different. Tomorrow, it will be something else entirely.
The overall pattern here is that of gratingly-repetitive bullshit-grade jargon that doesn't fit to begin with.
This has been my experience as well. It's incredibly grating to repeatedly read "genuinely load-bearing", "honestly?", etc. I've tried to get Opus to stop using these phrases via an entry in its memory, with mediocre results.
I don't mind interacting with LLMs myself and find they increase my productivity a decent amount. I just can't stand dealing with other people's slop.
Getting sent IM responses that are copy pasted LLM nonsense. Getting a massive PR to review that was generated overnight and the author didn't read it first.
It's burnt me out too. I'm generating 10x more features and multitasking across 4 disparate projects. My greatest concern is I don't really have a strong connection to the underlying fundamentals anymore. I need to see how the things works to internalize it. Now I just trust that the agent wrote this piece correctly.
The productivity drive and the sheer feature set you can generate in record time makes it easy to forget proper sdlc hygiene.
I don't really understand how this isn't a self-inflicted problem? Perhaps it's because I'm not really mandated to use LLMs in a particular way, but I've had great success doing a combination of writing code myself and using smaller but faster models as a sort of "flood fill". The larger models can also be useful when you're implementing something which already exists in similar form in the codebase, because you can just put that code in the context and you'll get something very similar outputted. So the more code you write, the better the LLM can be later on. Codebases should get easier to add to the bigger they get, not harder.
Of course if you're supposed to achieve so much output that it's not possible to do anything but vibe it, fair enough.
started to dread reading LLM output because I know what I’m going to find. False assumptions and hallucinations. Emphatic, staccato fragments. Excessive emojis .
I do not understand these complaints. Yes, those are the defaults and they're annoying, although the general public seems to like them. But you are not stuck with these. You can just tell the LLM how it should interact with you. If you're using any sort of harness beyond the chat window in a web browser, you can codify these instructions in a rules.md file or similar and have it automatically included in any new chat. It's not any harder than changing the default wallpaper or color scheme on your desktop operating system.
In reverse order, you can just tell the LLM to never use emojis. I don't like emphatic staccato fragments either, so I tell it to eschew the language of marketing and hype and stick to a factual and plain language, or to employ an academic tone. I explicitly instruct mine to ask clarifying questions whenever context is ambiguous and to push back on false assumptions or common misconceptions (by me). Hallucinationsa re the biggest problem of those you mention; it's not easy to totally eliminate them (for the same reason it's not easy to instruct people to not fall for scams or disinformation), but you can considerably reduce them by setting standards for citations.
I have ideas about reducing hallucinations over work material (ie a codebase) but am omitting them here as they are not fully thought out or tested.
is there any evidence that Alec Scollon, the first time blog author responsible for this post, even exists? look up the name. boo this post and the premise behind it.
Define "exists". Are you questioning whether a human typed those characters with human fingers on a keyboard? Or are you questioning whether Alec Scollon is the name on that human's government-issued ID? It's not exactly new or unusual for people to use pseudonyms.
From my experience, there are mainly 3 burnout reasons.
1. Multi-tasking is the top one. I usually have to frequently switch between 3 to 5 agent windows which are on different things. It's extremely exhausting when each round takes a few minutes. Before coding agent era, I believe most developers had chance to spend 2+ hours focusing on one thing. Now coding agents have increased my spectrum on the tech stack, but the bandwidth to do deep work isn't increased.
2. Agents are good at getting things running without crash, but do not guarantee to produce correct code. This is quite different from human experts with fundamental knowledge.
3. I also get frustrated when reviewing piles of AI generated low quality PRs. My attention is a limited resource. I don't waste too much energy on other people's work, but if I don't spend more effort, the entire project is corrupted quickly by reckless AI generated code without human author's careful thoughts and designs. Working with people who have less due diligence in mind is painful, working with them in coding agent era is 10x painful because they produce 10x shit. It's a team culture challenge that cannot be easily enforced.
The reason I'm getting LLM burnout is from dealing with the obvious neutering and opaque downgrading of all the top models.
Prior to the last 12mos AI companies were hell bent on squeezing out the best results from mediocre models.
But... now that the top models have progressed, those same AI companies have switched their efforts into reducing the computation (cost of a producing a result) as much as possible without being too obvious.
What was an exponential slope in the quality of results over the last 36 months has now nearly flat lined.
Addendum: IMHO results have 'flat lined' not because the models aren't much more capable than a year ago, but because conserving the enormous processing cost (of an over subscribed user base) supersedes the goal of following the user's explicit instructions (e.g. especially if that means more processing cost) to generate the best results.
I feel the same way about consumer AI tools now. Gemini and ChatGPT have been abysmal lately. They can no longer be relied on to do multi-turn searching and thinking.
Before, they could stay in thinking mode for more than 7 minutes. For example, "find a source for this claim" would search, analyze, and self-adjust the query. Nowadays, even if I push for it, I cannot make these tools work for more than 30 seconds before they give generic answers, even in "Pro" mode.
Hell, the Opus 4.5 moment was only last November, and that was when agentic coding and most coding CLI tools became truly first class options. That's a wild paradigm shift. Hell, GPT-5 wasn't even out (that's August of last year). Most people were using 4o. Their current offerings are wildly better for coding than 4o was.
What helped was a sleep and work system, oriented around being offline that was inspired by nature and from my earlier days in working in tech while car camping across the national parks.
Basically: the sun wins in terms of how all energy on the earth is structured, and expressed. All manners of cycles of organisms and living systems are in relation to its rise and fall, and even its particular color spectrum phases (whether thats night oriented or day). I call this our real circadian rhythm; it's used to being signaled by the light of the sun and maybe fire for millions of years and it isn't until recent centuries when we started tricking our biology with LEDs and lights. So the solution is simple. Orient yourself around the light of the sun and make sure it's the first and last major light source you see; blue limiting is the most important part BEFORE sunrise and AFTER CUT OFF ALL BLUE LIGHT. On my Mac I use a red light filter (using it now, it's 11:07pm ET and the sun went down about 2.5 hours ago). It's really hard to stay alert and chatting with an LLM when the only light sources are red and you keep them dim at that. Our ancestors would rest when the sun's at its peak (~1:05 pm today) and that's a good time to divide my own day productively as well. With intentional breaks diving the middle of the day with sunlight anchoring it, my nervous system is more relaxed, and by the evening time, it's also ready to transition out of anything blue-light assisted and most intellectual work and problem solving falls into this bucket. It's really hard to explain but it really works so simply. To enjoy the process a little more I made this fun sun clock, check it out at https://sunsignal.app
I once experimented with beeswax candles as my only after-dark light source. This meant no hyper-stimulating screen activities whatsoever, too. TV, phone, video games, browsing the web? Nope, nope, nope, and nope. Just dim, warm light from actual flames.
Cured my lifelong “night owl” “trait” in a couple days. Shockingly effective.
Turned out to be hard to keep up and still, like, exist with other people, and you’d probably need to relax it a little in Winter unless your job lets you work reduced hours to kinda “hibernate” (otherwise when would you do anything that’s not work but requires light or electronics?) but it sure worked.
That's so cool. And +1 to Beeswax candles. I have a dream we will orient ourselves with greater agency and autonomy and after traditional careers die if we get a UBI and still choose to work we would orient ourselves around the sun naturally and have time in the day for social lives.
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[ 7.1 ms ] story [ 146 ms ] threadhttps://github.com/JuliusBrussee/caveman
It's for getting it to output shorter answers, but also could help with your burnout.
I got into programming because the problems of programming were interesting to me. But if the problems go from "figure out why this calculator is off by one in France" to "Get this LLM to stop spamming cutsey emojis", then maybe it's time for a career change.
this AI bubble will pop. when it does you'll be hot stuff all over again.
Just because we work with computers doesn't mean we don't take, er, social-damage. Or perhaps parasocial damage, in this case.
You'll frustrate yourself by not using this tool without above. You'll definintely frustrate yourself expecting the tool to be "genuinely sorry"!
Anyone else working on something like this or know of any projects attempting it?
I've taken a bit longer than I wanted but it will be open sourced soon.
It's a durable orchestration engine that takes in specs/requirements and coordinates agents externally (meaning the engine drives the loop, not an agent) until the work is fully implemented/verified and reviewed.
It's meant to be used with any harness as basically the last step. You plan your work with whatever LLM you use and then hand off implementation to the engine (through an MCP server or other surfaces)
It can use your OpenAI/Anthropic subscriptions or any other provider and you can mix and match models across implementation and review in any way you want with fan out for parallel reviewers and more.
The goal is to produce high quality unsupervised code that matches your requirements and is reviewed throughout the implementation rather than at the end only, so that mistakes don't compound.
https://engine.build if you want to get notified when it releases.
I am happy about all the little side-projects, and ideas it help my realize, and I enjoy exploring this new world, but I've noticed LLMs feed my unhealthy "don't want to take a break and waste time being idle" mindset, and I need to correct it.
W.r.t. article's main complain - I think the similar thing happened due to factory manufacturing automation. What used to be a varied skillful craft in a shop became standing in a single place of an assembly line doing the exact same thing whole day. LLM took away the more creative and variable part of the work, and left the repetitive QA rubber-stamping. Probably some of the mitigations used back then could be rediscovered today.
I see a different type of pressure: I'm at a company that still is requiring everyone use LLMs with token leaderboards, time-spent measurements, and impacts to performance reviews, and all that. So I find myself having to carve out some percent of my time to stop doing productive work, and "go do AI to show token use." So my workload hasn't changed (or it's gone up), but I have N% less time to work on it because I have to spend time appeasing the AI gods...
Same, but I really have to fight the urge to just add fun new features to things I work on any time inspiration strikes. I am an appalling 'feature factory' if I don't actively keep myself in check. The cost of just building everything is so low, but the value of those things is also incredibly low, so I'm often just bloating what I build.
There's been a lot of articles and posts about the increasing importance of 'taste' in software built with AI, and I'm finding I know need to look for strategies to find some.
I had to think of the factory scenes in Charlie Chaplin's Modern Times. The author's feeling is basically the main idea of the sketches, i.e. humans having to follow the pace of the machines instead of the other way around.
Reverse centaurs are nothing new. Ask any worker movement from the last centuries.
“I wanted a machine to do the dishes so I could concentrate on my creative work, and all I got was a machine to do my work so I’m left to wash the dishes.”
Embrace it if you’re like me and feel uncomfortable having an idle mind, embrace it! You’ll get more done and being 120% go go go is impossible over the long run so eventually your body will just say I need a break then once you recover full steam head again on the treadmill
Yes, this is to me the primary driver of the extreme AI burnout. In ~30 years in Silicon Valley and many, many startups, the pressure has never been as intense.
Before AI I'd mostly work on one thing at a time (at least within a given hour) and in the evening I wouldn't start a new 6 hour task because it's too long, so tomorrow is another day.
Now, that 6 hour task is more like 30 minutes, so there is intense pressure to just knock it off tonight. And then the next one. And one more. And while the bot is thinking, to have 4 other work streams in parallel so there is never, ever, a break in the day. The human mind is not built for 100% utilization 15 hours a day.
I find LLMs to help me manage the unrealistic workload I have, because at least now it's feasible instead of just getting more work piled on top of me with a never ending backlog (that people actually expect me to thin, not let grow). Add on top of that colleagues that would have death by commitee'd many ideas and now just have to argue against actual MVPs that work instead of ideas (or can be proven to not work and discarded without wasting time on them in some cases), and I don't even hate my job as much!
It's just that to ensure that the technology is not a net negative, I need millions upon millions of tokens every single day (tool runs, adversarial reviews, testing), but once you get that inflection point, alongside needing a good enough model, the floor for which currently I'd say GLM 5.2 on Max reasoning reaches, or use something like SOTA Anthropic/OpenAI models, it becomes a pretty good way of working. That said if you have missing pieces there (e.g. using cheap models that aren't very good), the curve of getting stuff done can go downwards and you'll just end up with a lot of slop - useless docs, bad code and an ever increasing amount of technical debt.
On average, each task that I do, needs about 15 minutes to 2 hours of planning and making the agents explore the codebase and refine the plans first.
Curiously, in my case this leads to less burnout cause I can actually pause and grab a drink, meal or go for a walk, while parallel agents do the work, once I've planned things well enough and have dispatched something that will work for 1-4 hours. I don't have to review their output immediately once they finish but can just batch things.
AI coding is addictive. Engineers are paying the price https://leaddev.com/ai/ai-coding-is-addictive-engineers-are-...
The bots (all of them) seem to show patterns of overuse of specific phrases, words, and punctuation.
Some of those are the ones you mentioned. Another that I've been seeing lately is overuse of the term "gate", wherein: As a human, I know what a gate is. A gate is a thing that can be open, or that can be closed. It might be locked or unlocked. The path beyond the gate may be passable or impassable or nonexistent. The gate is just a gate, and the presence of the gate doesn't imply whether it is open or closed.
But in bot-speak, a gate only refers to a hard block -- an impassable construct. Like a fence or a wall, or even a lava-filled moat.
But while a lava-filled moat is intended to be impassable, the bot uses "gate" -- a thing that is designed to be passed -- to describe that same kind of obstacle.
That's misuse of the term, I think, based on decades of dealing with gates in reality: Usually when I encounter a gate that is closed, I just open it and walk through.
I do have instructions that tell the bot to avoid that usage of the word and it ignores them sometimes anyway.
But "gate" is just today's problem-word that comes to mind as I write this. Yesterday, it was something different. Tomorrow, it will be something else entirely.
The overall pattern here is that of gratingly-repetitive bullshit-grade jargon that doesn't fit to begin with.
"And that's the real, no-nonsense truth!"
Anything written for humans should be written by humans.
Getting sent IM responses that are copy pasted LLM nonsense. Getting a massive PR to review that was generated overnight and the author didn't read it first.
My mind still can't function well without having knowledge about everything.
The productivity drive and the sheer feature set you can generate in record time makes it easy to forget proper sdlc hygiene.
Of course if you're supposed to achieve so much output that it's not possible to do anything but vibe it, fair enough.
I do not understand these complaints. Yes, those are the defaults and they're annoying, although the general public seems to like them. But you are not stuck with these. You can just tell the LLM how it should interact with you. If you're using any sort of harness beyond the chat window in a web browser, you can codify these instructions in a rules.md file or similar and have it automatically included in any new chat. It's not any harder than changing the default wallpaper or color scheme on your desktop operating system.
In reverse order, you can just tell the LLM to never use emojis. I don't like emphatic staccato fragments either, so I tell it to eschew the language of marketing and hype and stick to a factual and plain language, or to employ an academic tone. I explicitly instruct mine to ask clarifying questions whenever context is ambiguous and to push back on false assumptions or common misconceptions (by me). Hallucinationsa re the biggest problem of those you mention; it's not easy to totally eliminate them (for the same reason it's not easy to instruct people to not fall for scams or disinformation), but you can considerably reduce them by setting standards for citations.
I have ideas about reducing hallucinations over work material (ie a codebase) but am omitting them here as they are not fully thought out or tested.
Prior to the last 12mos AI companies were hell bent on squeezing out the best results from mediocre models.
But... now that the top models have progressed, those same AI companies have switched their efforts into reducing the computation (cost of a producing a result) as much as possible without being too obvious.
What was an exponential slope in the quality of results over the last 36 months has now nearly flat lined.
Addendum: IMHO results have 'flat lined' not because the models aren't much more capable than a year ago, but because conserving the enormous processing cost (of an over subscribed user base) supersedes the goal of following the user's explicit instructions (e.g. especially if that means more processing cost) to generate the best results.
Before, they could stay in thinking mode for more than 7 minutes. For example, "find a source for this claim" would search, analyze, and self-adjust the query. Nowadays, even if I push for it, I cannot make these tools work for more than 30 seconds before they give generic answers, even in "Pro" mode.
Hell, the Opus 4.5 moment was only last November, and that was when agentic coding and most coding CLI tools became truly first class options. That's a wild paradigm shift. Hell, GPT-5 wasn't even out (that's August of last year). Most people were using 4o. Their current offerings are wildly better for coding than 4o was.
What helped was a sleep and work system, oriented around being offline that was inspired by nature and from my earlier days in working in tech while car camping across the national parks.
Basically: the sun wins in terms of how all energy on the earth is structured, and expressed. All manners of cycles of organisms and living systems are in relation to its rise and fall, and even its particular color spectrum phases (whether thats night oriented or day). I call this our real circadian rhythm; it's used to being signaled by the light of the sun and maybe fire for millions of years and it isn't until recent centuries when we started tricking our biology with LEDs and lights. So the solution is simple. Orient yourself around the light of the sun and make sure it's the first and last major light source you see; blue limiting is the most important part BEFORE sunrise and AFTER CUT OFF ALL BLUE LIGHT. On my Mac I use a red light filter (using it now, it's 11:07pm ET and the sun went down about 2.5 hours ago). It's really hard to stay alert and chatting with an LLM when the only light sources are red and you keep them dim at that. Our ancestors would rest when the sun's at its peak (~1:05 pm today) and that's a good time to divide my own day productively as well. With intentional breaks diving the middle of the day with sunlight anchoring it, my nervous system is more relaxed, and by the evening time, it's also ready to transition out of anything blue-light assisted and most intellectual work and problem solving falls into this bucket. It's really hard to explain but it really works so simply. To enjoy the process a little more I made this fun sun clock, check it out at https://sunsignal.app
Cured my lifelong “night owl” “trait” in a couple days. Shockingly effective.
Turned out to be hard to keep up and still, like, exist with other people, and you’d probably need to relax it a little in Winter unless your job lets you work reduced hours to kinda “hibernate” (otherwise when would you do anything that’s not work but requires light or electronics?) but it sure worked.