I always find so much cognitive dissonance in these kinds of posts.
> My most successful work this year was hand-writing a tool, replacing ~4,000 lines of vibe-code with a few hundred. My system has proven more flexible, easier to test, and more generalizable than what it replaced.
> agents still need human taste and experience to produce good work.
Where do you think human taste and experience in software comes from?
> overstimulating & exhausting in a way that coding never was.
> for many of us, honing the code, finding the right design, seeing the parts mesh together — that was the meditative purpose we found in our jobs.
> That’s not the only time AI waylaid me. LLMs could not figure out how to annotate that model’s training data correctly. I spent days going back and forth with them
> LLMs are not yet good companions when brainstorming complicated things.
Why do we expect this to get better? The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
> Writing code is solved.
What a strange thing to say given all the above experiences, which I think we all share.
Why are we willing to give up so much in terms of purpose and understanding for perceived productivity gains?
This always reads like “writing english is solved” just because you collected snippets of famous works and have built a spell corrector.
Code is written to process data. The struggle was never to learn programming syntax (most grammar are only a few page long). The main struggle was that it’s hard to model a domain into an information system, identifying the components and their relationships, what is ingested, and what needs to be output.
Computers have integers and floats, but you need to implement units like distance, money, various counts,… The real world have words and sentences, but that needs encoding like ascii and unicode as well as the “string” concepts to make it work. Take HN, I’d bet that if you can model it in terms of data, writing code is not an issue.
> The struggle was never to learn programming syntax
For 85-90% of devs, yes this was the struggle. Creating and updating rows in a database from user input is all the “domain modeling” there is in most software. That’s what is solved by AI and it’s the majority of software dev.
Learning syntax takes a weekend. Learning to approach problems analytically and algorithmically takes much longer, and is the only "hard part" of computer programming.
It certainly wasn't "approaching problems analytically and algorithmically" that prevented me to contribute to many open source projects in the past.
On the other hand, I've made 3 apps in 3-days' spare time, which I could not find in years and were not worth investing months of effort at the time.
It takes a weekend for a software developer. For a totally novice it takes a lot more time
The issue here is that you are learning the syntax at the same time you are learning foreign concepts, and the syntax is often confusing and counter intuitive
Learning the syntax separately is of no use because without a mental model you can't do anything with it
I remember learning python in high school by following the tutorial in the official docs, then from a book. Up until function and OOP, it was fairly easy, but I gave up afterwards because the exercises touch on things I haven’t learned yet like trigonometric identity. I could see and understand the syntax, but I couldn’t understand the code.
This is why children can easily learn enough programming to create games, because it’s a domain they can comprehend easily. Just like they can use english to write about their daily lives. It will be simple sure, but it will work.
So learning to code is fairly easy, you can easily create simple programs after a weekend. More complex programs will still use most of the same basic concepts as far as coding goes, but you will need to master more complex concepts, and that takes time. And such concepts are not about coding, they are about the nature of computing or the domain the software is created for.
This is why experienced devs only need a few days doing kata to get up to speed with a new programming language. It’s usually the same old, with slight differences. What was always needed was an understanding of computing and the ability to break down problems and create a solution that can be computed.
Coding to me is the same as drafting an engineering schematics, or writing a sheet music. It’s just a tool, not the end result.
Thank you, this is what I've been seeing. Structs and objects are easy, but the hard part is encoding the domain most accurately into given data types of the computer (or creating new data structures to solve the problem!)
If anything, I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head.
Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
> Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
Even with basic pseudocode and very lenient logical rules, most people won’t bother to provide a a good definition of some process, as it’s quite unnatural to be that logical. Instead, it’s mostly just playing around. When you ask the one that is supposedly 10x-ing his productivity to explain his mental model of their own projects, it’s often a wishlist instead of a real understanding of what he has built.
> I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head.
Those are the people who are most likely to give a monolithic prompt and then spend a week telling the machine 'I don't care about encryption or file permissions, just make it work.' Not to mention that most people can barely verbalize the problems they have with devices they use for 8hrs a day, let alone form a full, self consistant, project idea and then put it in writing.
if they're persistent and patient enough to see the result of what they think they want and iterate enough times they'll eventually learn. non-coders aren't dumb by default after all and I suspect they'll learn much faster than us because they won't be bogged down by the minutiae like reading docs.
Imagine you could build a piece of furniture without actually needing to know how to cut wood, drive nails etc. Imagine you could do it in 3d without knowing how to actually handle wood, you just have an image you can iterate on without actually costing you anything but time.
I remember the first big app I wrote in high-school. I had to learn C++, learn Qt etc. I knew what I wanted but putting that into code when I didn't know any of the libraries / tools was a huge time investment. If I had LLMs I could've done the same thing it took me 6+ months in probably less than a month.
just because you have an idea doesn't mean it will work. kids come up with ideas all the time in their drawings, but they disobey the laws of physics and are therefore not feasible in reality. sure, the model can guide them, but eventually they would have to pick up the necessary knowledge.
The necessary knowledge needed with LLMs goes from needing to know programming languages and libraries to having a high level view of what is possible which is far easier and less boring to get
> The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
RL is not dependent on training data. The mathocalypse didn't happen because they found better training data somewhere.
I’m not sure that’s a good argument that models will get good at novel brainstorming and research ideation. Where do you get scalable and high signal verifiable rewards for creativity and insight?
> The mathocalypse didn't happen because they found better training data somewhere.
There are reasons to suspect that OpenAI may have intentionally or unintentionally trained on chat logs from mathematicians--mathematicians who were actively using OpenAI to try to to solve the same problems themselves.[1] If those mathematicians were on the right track, and getting close to a solution, that could be classified as finding "better training data somewhere."
sorry but
given how post-training actually works, there's essentially no direct way in which literal PII-scrubbed data made its way into a production oai datamix for RL warmup purposes in 2026 to the extent this data has influence, it's indirect/at the level of process decisions.
what i am trying to say more specifically is, the folk model some people seem to still have about ai companies "directly training on user transcripts" in this naive fashion is firstly wrong, and secondarily, would be counterproductive as a strategy for continued progress
"transcript shows human trying to do an exotic thing evals don't currently cover -> build synth constructions that broadly cover its genre of problem class -> RL on the verifier set designed to cover the failure"
that's the loop...
the """flywheel""" is... diagnostics! wow!
and so this kind of process improvement driven use of data has an obvious diffuse causal influence on what happens during training, right?
but ~none of that process influence looks like "specific mathematician's specific intuitions leak into the training data".
It’s perfectly plausible that unscrupulous people at OpenAI (and that’s the culture right from the top) searched user transcripts for relevant approaches to problems they were working on, or searched them for problems to look at, and nudged models based on that.
I agree it’s unlikely this happened by accident, or entered training data (though not impossible), but it could certainly be within the context agents were using when tackling problems.
We have no evidence either way, but they had all the data they would need to do this.
Rl works on one of two things - (1) a literal set of rules for a system or (2) training data. The former matches perfectly to things like (most modern) math and games that have perfect sets of rules. Even things like physics and robotic where our rules/models are extraordinarily good, rl barely adds value. Move one step farther to chemistry or a complicated physics scenario like weather, and you are back almost entirely dependant on (2) data. Now there might be a few more domains with rigorous rulesets that haven't been explored fully (id bet with some effort programming might make some strides here, maybe something like circuit layout, maybe even law if folks decided to make a lean-like law scheme...) but the vast majority of problems AI is targeting are dependant on data.
First of all ppl are just processing the new enironment. So go easy on conclusions. Its all in flux.
Secondly most the posts on HN are from individuals. It doesnt represent group dynamics and how group/institutional knowledge works. For example there might be a regulation on publishing data every quarter. On the surface it looks like compliance but in reality different internal groups will hide data, delay it, fudge it etc etc. LLMs train on public data. Only after they are allowed access internally do they discover group knowledge. We are far away from seeing that happening.
I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with. I have seen what the best models can do, and I've seen what people call "good", and I am not impressed.
However, I'm still split. Because this could mean that we see a jump in code quality, due to bad coders getting someone better to code for them. But we could see a decrease in code quality, due to less people "caring".
Code written by not very good coders has the possibility to be short and to the point. Even if I think the architectural decisions are wrong, the code still communicates a motive and an understanding of the problem at hand. This is all lost with LLMs.
I'm curious, what language do you code in? I use Go for a high scale backend service at this point it is better at writing code for a jira ticket than most of my team.
There is still questions about if that jira ticket should have been written but from ticket to mergable PR it's better than me.
In my experience, it's very good at following standards, so good in a structured project. Not so much in a freeform project where things get out of hand pretty quick.
Most people I've seen saying coding is solved are ML engineers who write throw away code for running one-off experiments. If you can effectively "archive" code after getting the results you need, I can see why you would say coding is solved. Sustainment is a non issue.
> I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with.
I’m probably the best coder in my org, which doesn’t really say much, but at least allows me to judge about the room temperature. It is solved. I don’t say that’s you, but the only people who say it isn’t “that good” are the same people who used to yak shave, rename and move things 10 times until it “feels right”.
Ah, I think some people who say "coding is solved" implicitly also mean "you don't need to read the code that AI generates". It's not a super clear proclamation, but it is certainly provocative!
I've had a realization recently. I was also probably the top craftsman in the teams I worked in throughout my career.
I think what people are missing is that people often become craftsman because we want to be able to keep the code adaptive and performant so we can provide business value quickly and efficiently.
The change is with models, the benefit dynamics of immaculately structured code changes dramitically. Now the real area you need to focus on is correctness and performance. Structure doesn't really matter unless it serves those two purposes, at least from the business value side.
There is no substitute for reading the code in many many cases. I spend more than 3k/mo on tokens. I find incorrect assumptions baked into the code (the ultimate spec) and missed by the agents and tests daily. This is more pronounced in data analysis and complex systems.
As a side effect having to read the code at least once inevitably makes it way shorter and usually much better.
That's right. And you have to read what the agent is doing as well. Once I said (Opus 5.5), given this list, find issues that are both critical and related to {X feature}.
Guess what: because I said "both", Opus decided I was looking to pick out exactly two of the issues. If I hadn't read that reasoning log, I would've remained oblivious and thought there were only two it could find.
To be fair, that's a pebkac problem. You got exactly what you requested (and the language was unambiguous).
This is like writing `if (cond_x && cond_y) {}` and then blaming the compiler because it didn't realise that actually meant to write `if (cond_x || cond_y) {}`.
That's not what they said, they said it limited its output to 2 issues because it saw the "both" in the sentence, which is clearly an incorrect reading of their instructions.
You're right, but I do wonder if they're misremembering their prompt a bit. The AI's response would be much more likely to arise if the prompt was something closer to "find both issues that are critical and related to X". Based on my own experience, it would be a very surprising mistake for the AI to have made if the prompt really was as straightforward and precise as "find issues that are both critical and related to X". I know it's a bit annoying to question someone's account of their own experience, and obviously I'm not saying this couldn't have happened, but my guess would be that the actual prompt was at least somewhat ambiguous.
OP: [last year][1], I shared how I was using AI day-to-day as an ordinary engineer. This year, I did it again!
It's crazy realizing that Claude Code only started getting big in the last year or so. It feels so different now, with all the new models & tools we have to use. I hope you enjoy!
March 2025 was the start of the boom, IMO. Sonnet 3.7 and CC released feb 24th, Responses API + Agents SDK from OAI launched March 11. Two months after we got the first Terminal Bench, then a month later we got tau2 bench.
I guess, its debatable that o1 was the real start- but I think its hard to deny that Claude Code CLI was the first time we had what we now call a coding harness hit the mainstream. And- Sonnet 3.7 was kinda Anthropic's "o1 moment".
God. Opus 4.6 and 5.3-codex feel like ancient history.
AI, like people, seems to get better as experience builds up. (thread context) But that experience is getting used to the workflow that fits that user, so it seems hard to know in advance. So, like the post said, since you only learn the better approach or structure "after doing it", this seems like a pretty common situation.
Whoever says they throw agents at work they (the human) are getting paid to do, i am very curious about how you make sure the thing that is being built is it's money worth.
Im doing this, against my will i preffered an anchored approach but well the thing is i work for a rich guy, like most of us, i dont care the thing has 'money worth' i only care the rich guy gives me a fraction of the money we produce.
But still, if the thing is really bad it just makes my work harder so im trying to keep it stable, i am still reading the core of the system as much as i can, the agentic pipeline in this case.
I have different reviewers working, custom company model, claude, codex they run in different stages of the projects like for SOLID, for linting, thats what we build the review agents for.
I check the architecture and design documents throughly before starting, then the AI creates documents that the AI will consume and i check the details didnt changed, the same if you need example proof of concepts, like some inherited approach from the anchored spec driven times.
At this point reading every line is not possible, not even worth it to discuss it at company level its not happening, so these kind of things are your safety net now so if anyone has other strats please do share them i think we all need them.
The "Art & music & video" section is really puzzling to me. On one hand, the author mentions liking some AI music, and then links to low-effort songs that at best you could call sort-of catchy... and on the other hand, the author writes about AI video: "Has it ever been used for good?"
Gen-AI can and is being used to make real, worthwhile movies. The best video model available, Seedance 2.5, can't produce clips longer than 30 seconds. The human artistry comes from which clips/shots you ask for and how you edit them together to tell a story. Plenty of people are doing good work here.
Here are some examples. Even if you don't like these personally, they aren't slop. You can feel the creator's intention behind them:
Watched the last one. Why are they drenched in sweat all the time? Also amusing they're still wrangling with certificates, a true dystopia!
I mean it's a video alright, if that's the point. Frame by frame imagery.
I enjoyed yours. TBH, I feel like some small thing is still missing. It's difficult to put the finger on it. Maybe the dialogue isn't hundred percent credible? Or maybe there is something subconscious where we notice the "actors" are not fully believable. But I think you've got talent.
What struck me is that it's really close to photo realistic, but I would still have noticed it's AI by the tiniest continuity mistakes and visual cues, like slightly unnatural color grading and such. However, if I had seen this five years ago, I would have taken it to be a real recording with post processing that makes it look a bit polished, like Benjamin Button or Grand Budapest Hotel.
Yeah there are definitely things that don't feel quite right. Part of that was my own shortcomings and the time limit on the project... but I think the tools are at the point now where with enough time and attention they can make something that feels totally real.
Nothing against the post itself, but something I find funny: the development in AI is so rapid, that everyone thinks they are ahead of everybody else. Here we have a blog post telling us "coding is solved". In Oct 2026, how many of us don't know this already?
The structure of the post seems like a comparison of snapshots between a date this year and last. On HN, it's an odd statement. In the context the author is writing in, it seems fair.
Find a lot of tech adjacent jobs on reddit and see them talk about being outskilled by new hires using AI to automate sysadmin tasks. I used to a sysadmin very early in my career and given the tools today and the problems back then, a lot of these jobs are going to be obsoleted or dramatically automated to not require FTEs. This is already started in those fields. Powershell and bash scripting is very good from these things and has been for longer than a year IMO.
We're in our own bubbles where a good part of HN population woke up to coding agents being good enough ~1 year ago, there's a group of folks that found out ~6 months before that, and then the people banging their heads on these a bit earlier than that. If you aren't using a coding agent everyday most people are just using these things to apply an instagram filter, or sort a grocery list. Muse is probably the closest thing to 'full automation' a lot of people are seeing for the first time.
Unfortunately I don't think the bubble that exists at the moment is on the promise of automating so much coding as trying to penetrate consumer use cases, which I think are still lacking and not reliable enough yet. As much as I use coding agents every day I still will not let a claw-like near a bank account or a credit card.
The problem with Muse and similar "run your life" agents is that booking a dinner reservation or signing up the kids for soccer isn't anything that I need to automate. They need to find better examples of real problems for their marketing.
This was Gemini the other day: https://imgur.com/a/7mdqwjk — this wasn't some errant load, even refresh, hard refresh, etc. got the same result for a while. An outage, AFAICT.
While trying to capture that bug, I (ironically) asked Gemini (but the Gemini-via-Google-search Gemini, since Gemini proper was an outage) for the package name of a screen recorder (gtkRecordMyDesktop); the command it returned referenced a non-existent package¹. I then found an alternative, and then hit a bug in that screen recording software that I had to then work around to get the video file there.
The site I uploaded the video to, Imgur, breaks completely — i.e., it cannot even so much as surface an error message — if your browser refuses to let it store god knows what on your disks. (So as it is, I have an extension that says "why yes, of course you can have storage, and essentially provides a black hole. But since it looks like a "success" to the Javascript, the JS doesn't crash!)
This isn't, to me, some one-off interaction. Every interaction with software these days feels like this, more and more. Just breakages everywhere that leaves me wondering if "coding is solved", where is all the bug free software?
(¹forcefully prompting the AI to actually query the package listing netted me a classic "You're absolutely right! The package name I referenced does not exist!")
Yep. Everyone is saying that AI is writing all this great software. Where is all the great software? Really, where?
My experience is the same as yours. Every single service I use. Apps, websites, doesn't matter what. Every single one seems just softly degraded. Like I can't trust that features work any more. I could report the bug, but what's the point? It's going to go to some Claude triage agent who is going to "fix" it by slopping up the app even more. Do people even use their own websites and apps any more? Apparently not - the same people declaring software solved are having their AI agents doing computer use and going on these websites for them!
Claude, please read the Bible for me. Tell me what I need to do to get into Heaven. Oh, can you dictate it to me? I'm managing an agent swarm.
You mean the three new products we launched in a month instead of a year (or two..)? Sales are good, customers are happy, we are doing well.
I know, I know, we are shit and AIs are a godsend for small shitty companies that have at least one clever person that architect their way out of a paper bag. I’m sure all ya’ll highly competent, genius academics don’t need it and find it a hindrance but the devs in my bubble really, really need it. And there are a lot of those..
They just live in denial. I don’t have any proof, but I suspect those are the same people that would nitpick your MR to death until you hint to their manager that this is not ok.
> Every interaction with software these days feels like this, more and more. Just breakages everywhere
I've noted the same but even before llms so I can't draw a conclusion there. I find strange issues with software I cannot explain. Not only breakages or bugs but also a strange lack of core features and UI/UX that leaves me wondering "am I dumb?".
b) you confuse technical quality with product incentives
If you see this, company doesn’t care about product. And Google doesn’t care about quality for at least a decade now, all they care is a share price and speed of delivery.
> In Oct 2026, how many of us don't know this already?
The comments to this article suggest that it's still disputed.
Personally, I think it's a bit of a silly phrase, because it implies an absolute, and all anyone needs to do to disprove it is to show that coding was not solved for problem X. That's still the case -- it's just that the set of X is rapidly shrinking.
I think it's more indicative to say how much you trust LLM code. A little over a year ago, I scrutinised every line carefully, and it was buggy as hell and I had to fix most generated code by hand. Now, models write clean code with fewer bugs (far fewer) than I would. A year ago, I was doing a lot of steering during planning; now, it's rare that I don't agree with the model's first proposed solution, and even if I don't, it's most likely to fix a minor change in understanding, not to propose a substantial architectural change as it was before.
The one thing I'm not sure about is how much this is due to better prompting on my part. I doubt that this contributes much, but I still suspect that continual usage does make you more aware of what works and what does not: how much detail to provide, how to describe a problem or change, how to propose solutions while still leaving other options open.
Coding is solved in the sense that you don NEED to write code anymore. Of course you need to check and design and find better ways of doing things, but nothing of this requires you to write the code directly, you can always design, check and ask the LLM to implement a new version.
I have tons of acquaintances like that.
They vibe-code and think they've gained an advantage over those who don't know how to vibe-code
But there's nothing to know — take a 3-hour course, pay 200$ a day for Claude, and there you are, a vibe-coder.
It's just that most skilled developers find it disgusting, because the AI spits out code that doesn't work the way it should, and it all turns into the routine of "threw in a prompt, waited, reviewed, repeated." — only those who were crappy programmers are happy about this.
Always have a good chuckle at these doomerism bullshit posts. Our VPs are so busy it is impossible to find a meeting time, but suddenly they’ll develop ADHD and start vibe coding enough to replace 100 devs. Lol!
With all the time and resources Google has put into their products, you can probably conclude that either a) they want it that way, or b) no one there wants it to be otherwise enough to take resources away from whatever they were doing, and put it into that.
I guess humans have agreed, that art is a skill you must learn if you want to produce it, but engineering or software is not a skill worth learning and we are happy if computers can do that for us.
So it’s great and amazing if anyone can create their gizmos and software to help them but not if they create music or digital images, which apparently it is wrong to prompt, and not a sign of a great future.
Isn't this directly from that most people can relate to music/art but not with programming?
If you specified to someone that the AI is replacing the farting-into-mic-core music genre, they would not care. Same with programming, the average person is not exposed to this
This rings hollow to me for many reasons. When making CGI for movies quite a lot of the process has been completely automated for quite some time. So that scene in the movie is no longer art? Making video games you can use procedural generation to create a landscape, trees, rocks, etc. and most developers do at this point. Do we disregard all that when considering if the game is good or not? Or just blanket "it's bad because of that" like some people are doing with AI created stuff these days?
AI created art and photography keeps winning contests and people get all upset after the fact saying it's not real art. If it's not real art then why are the judges so impressed by it before they hear the word AI? I get the sentimental "but a soul that has experienced pain didn't create it" but if it's fun to look at and gives you joy is that not enough? And there is of course always a person in the loop creating it.
Definition of art:
The conscious use of the imagination in the production of objects intended to be contemplated or appreciated as beautiful, as in the arrangement of forms, sounds, or words.
A director imagines the movie scene and has a rough storyboard sketched, then CGI artists make it who then usually have some form of algorithm or ML help them. I'm sure most people would agree that's art even though the director only had his thoughts interpreted by many different people / machines. Why is that so different than a person asking ChatGPT or Seedance to create something they (and only they) imagined? Is it the effort involved? What if they spent 2 hours refining 1 photo to obsessively to get exactly what was in their imagination onto the screen?
I guess this is what is missing from vibed AI art. Usually it's someone with a quick prompt and nothing else. No effort or creativity from the human and that's probably what we don't like about AI art.
This is different from AI art looking all the same though
I kinda disagree with both points maybe because I've been using / seeing AI art extensively for a while. Some people put in absolutely monumental effort into their stuff, and if it all looks the same then toy story looks the same as ghibli looks the same as 1950s Disney looks the same as Wallace and grommit (all of which you can replicate fairly faithfully, combine into something that looks new, whatever else).
I think AI art is much like ai coding where people used 1 model a couple of times then wrote it off completely as garbage. Then years of very quick progress happened and all the 10 fingers on a hand and every picture looking the same disappeared.
If you can make it through the first 20 images on civit and tell me nobody is expressing themselves or their imagination in some of these pictures I guess we agree to disagree.
Because I’ve seen both and code created by your uncle might work and look good enough, and music and art created by your uncle is a fucking garbage that pollutes the internet.
> I also now reckon with the fatigue of using these agents. Managing 6 different conversations with 6 different robots about 6 different features is overstimulating & exhausting in a way that coding never was.
6 used to be the max for me as well when I worked with Claude Code in terminal windows. Too little to hold on to and to discern the conversations. Just walls of text.
I can now go 20+ with fun because
1. I built a canvas for organizing the work spatially, so every feature has its place,
2. I condensed sessions to a WhatsApp style chat with detailed agent steps available but folded away by default, and
3. Each feature agent ends with a demo, usually a narrated video that I can watch and comment on by voice as it runs.
(My solution is available open source and free: obeya.si)
$3,000/month is nuts! For $36k (1 yr of spend) you can buy a system with dual rtx pro 5000 (144GB VRAM). or spend a little more ($44k) and get two M5 Ultras w/512GB RAM. the latter, clustered, is big enough to run the biggest open weights (quantized), or smaller MoE weights on each in parallel. what you get for these systems is way more than $3k/mo of tokens, because you can run it 24/7/365. you can have pass after pass after pass of optimization, bug fixing, orchestrated subagents on backlogs, etc. way more value than paying for managed inference
Over the past year, we’ve been using AI to solve everyday challenges and troubleshoot issues we encounter in our jobs. Now, we’re taking the next step by leveraging AI to build solutions, automate workflows, and improve the way we work.
OK, yes, I am not a pro dev... still, that guy blows more tokens a month then I have consumed since the conception of GPT3! Granted, I stopped using agents a few months ago, when I got my first "you reached 80% of your monthly limit" message. Since then, I am manually constructing the context and executing edits... which brought my token bill down at least 5x and gave me 2x more useable code. But yeah, I am sure, 3k$ tokens a month is not wasteful behaviour at all :-)
OP: my company pays for my work tokens, which is the source of the $3,000/mo. I don’t use substantial tokens with my personal subscriptions, so I didn’t factor that into my number. But good point, I’ll clarify.
> since agents write everything, it is simple to propose sweeping changes to my teammates during code review, or even write my own versions — both things unthinkable in the era of hand-written code.
That is both untrue, and should be something that occurs rarely.
Past Mid level, most dicussion in code review is centered around the conceptual level. Code is mostly use to highlight the point being made. I’ve never seen the need for someone to rewrite a patch proposed by another person. Add to it or correct misunderstandings, but never sweeping changes.
Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
I walked into a paint store recently and there was some interesting music playing on the PA. It was some sort of spanish-language 80s new wave I'd never heard before. It had a nice groove. I asked the guy at the counter about it and was surprised to find out it was AI.
So... was it as good as great music? No. But it beat the hell out of muzak. And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
AI music is far less interesting than anything any person could create. Even if it's acoustically interesting, it's hollow and pointless because it contains no human expression.
AI music is a complete waste of compute. It exists only to further enrich an already wealthy stakeholder, and we should all be ashamed to have ended up in a place where it even exists.
It contains plenty human expression. It's ultimately made by people.
And I think it can be uniquely fun, because it allows for the very rare combination of truly off-beat ideas with excellent execution.
In real life, it very rarely happens that somebody will spend a lifetime learning, gather a band, and then record something extremely off-beat. Because there are risks to that. Destroying your image, having to find people that are on the same wavelength, having to justify the time and money it costs to record something weird and not marketable.
Sure, people make weird stuff once in a while, but very rarely, and generally it takes unique individuals that find some particular weird niche to work in, like Weird Al or Max Raabe. And even then they tend to pick something relatively tame and marketable and stick to that.
But AI can bypass all that, and that makes things interesting.
> My jaw literally dropped. Do you really believe this?
Sure. It happens in every field. When you invest a lot into something, it becomes risky or unprofitable to do some things.
Tech often drives things in the opposite direction. As it gets easier/cheaper/faster to create something, doing something odd and risky becomes easier to justify because there's less invested into it and there's fewer people whose cooperation is needed.
I'm not saying nobody does anything risky traditionally of course. But there's barriers to it, which become lesser once tech trivializes a good part of the process.
> It makes sense that someone like you would like AI music, I guess. You are clueless.
I like music in general, and am fairly indifferent as to how the goose is cooked. I listen to almost everything. There's no reason to exclude AI music. If it sounds good, it's good.
You need to listen more than the Top 10 charts my dude. The fact that you use Weird Al as an example of experimental musician says it all about your depth of knowledge.
The vast majority of music is made with no regards to marketability, it's just people doing whatever they personally like. An incredibly small amount of artists have any sort of "image", that necessitates being popular already.
And often the weirdest artists are also the most prolific because they tend to like experimenting for experimenting's sake.
Look, I don't know the song so I can't say how novel it is, but to me, it completely misses the point if its made by a music generation model. The point of music is not to make a bunch of novel sounds. Music is a means by which a human can express themselves, the whole _point_ of art is human expression.
Art is subjective. It sounds like AI music did evoke an emotional response from OP, which IMO is also just as important of what we define as the purpose of art: evoking emotion and awe from consuming art. A connection is being made from the creator to the consumer. I view AI as merely another tool to that means.
Human expression with art can go both ways from producing and experiencing.
Seems weird to contrast AI music with auto-tuned music. Most of the AI music I've heard has been tuned perfectly. And I don't generally have a problem with music being in tune.
For my part, I mostly dislike AI music. But if there was to be some AI music that I didn't already dislike for its content, then I would dislike it specifically for being AI. Call me close-minded if you will. Some people don't really like music at all. In general, I love music.
What about it? Microtonal music can be perfectly in tune. Further, when people talk about auto-tune, they're normally talking about vocals, notably absent from Angine de Poitrine.
> Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
> And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
For one thing, "interesting" and "novel" are not particularly the point of art. An infinite TikTok feed of AI slop is in some sense infinitely interesting and novel.
But also, those concepts are subjective. Pop music has a ton of evolution and innovation if you break it down. Sure, not every hit is doing something clever, but plenty of them are. You might not find those features to be interesting but it takes ingenuity to stand out in an era where the barrier to entry is so low.
>Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
>Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
Controversial take: For ages, it's been clear that most people have never given a shit, it just took AI for most to realize it.
I don't know what to tell you, but me and my kid are having a blast writing ridiculous songs and plopping them with song descriptions and some of those make it to our drive playlists.
Respectfully, the primary thing you are describing here is spending time with your children. Same could be said for spending time and attention discussing children's drawings.
The problem with AI performances is that they're too perfect, which leaves them flat. The dynamics and imperfections of human performance have yet to be replicated. That affects every human performance whether or not you notice you're noticing (but you are). AI is still in the uncanny valley here.
This sounds like CD music 40 years ago. All my friends said that it was not like a record and left an empty feeling in the room. I feel like this is not talked about any more. People listen to digital music and have forgotten there was an alternative. I suspect that in the coming years no one will think twice about AI generated music.
I find that AI should be used more extensively for System Design. I have seen a lot of people not use AI for that and come up with frighteningly complex designs and let their agents run loose to implement it.
Retroactively speaking, I engaged with the already agreed designs and used AI to find much better ways to do the same thing.
Overall, I wish people used AI even for System Design as that seems to have a higher impact.
It’s kinda nice seeing more nuanced takes from folks as the bubble kinda deflates and both sides realize their idealized vision isn’t coming to pass. AI isn’t going to replace all the jobs, but it’s also not going away; finally, it is merely a tool in our toolchest.
What does amuse me to no end, however, is how many craftsfolk in AI circles are loudly rejecting AI imagery and video in favor of humans. The only folks who seem fine with that are the grifters looking to slop out a bunch of adverts quickly for commercial gain rather than do actual art, or folks who want “art” but don’t want to pay a human to make it for them or learn the skills themselves. Both of those camps are viewed with disdain and loathing by both sides of the AI Boom/Doom arguments, so I’ll take whatever allies I can get on those fronts.
AI music…man, I live with artisans and musicians and we have a number of “artists” we suspect are AI but whose music is pretty solid. We’re waiting for the shoe to drop, but I’m trying to look at it as a kind of turbocharged Fruity Loops/Logic as opposed to a human replacement. I hope I’m right on that, but time will tell.
The game changer I think more doomers need to experience is agents. It sounds like a recipe for disaster (a bunch of models known to hallucinate and allowed to run roughshod over a computer unattended? What could go wrong?), and it often (hilariously) is for the scammers out there giving it full access to production and fucking off to hawk their latest Masterclass on X or LinkedIn, but that first time you give it a reasonably achievable prompt and come back to a halfway decent product you can review and refine into production in 15m or less?
Absolutely magical, doubly so when it’s just 27B Qwen via Hermes and not some souped up stack in a frontier lab costing you thousands a month in tokens. If I were to evangelize AI, that’s how I’d do it. Fuck subscriptions, fuck cloud compute, fuck frontier labs and token buckets and whatever slop Sundar/Scam/Dario/Elmo et al are hawking this week; run that locally, give it a reasonable prompt that solves a real problem, and show them the results.
Then do the right thing and go for a walk with the two hours of free time the AI just freed up for you. Don’t ask for permission to take back your time.
I’ve lived through three previous financial bubbles, and their most common characteristic was that they didn’t ‘kinda deflate’. Bubbles pop, dramatically, they don’t just deflate slowly. They don’t withstand shocks and bad news that have had the market up and down like a demented bunny over the last few months.
you can totally tell Claude to take some AI technical writing (which is, to be clear, writing where you explicitly need to not sound creative or interesting, and you need to maintain very strict "voice" [a term I only even know because claude explained it to me] so this is not a creative endeavor) and make it sound "less AI". At least working with opus 5.5, textual output already has a lot fewer of the colons and "load bearing" of a few months ago, and if a PR does have that kind of lingo I can just have Claude rewrite it "like a human" and it does great.
OP: I'm not surprised you/other people are able to get better output than me.
And I agree that "less AI" edit passes reduce a lot of the hallmarks, but I think these models truly struggle with expressing complicated ideas. Have Opus been good at that for you?
> I still believe I use AI fairly typically, but I’ve had a notable change in background. Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
Priest wears robes.
I guess it’s one of those nonsensical automatic rules.
> I am now a research engineer at Salesforce AI Research. I spend more time supporting scientists & building rapid prototypes, rather than working with a large team of engineers.
I appreciate the PoV of people with different challenges, but my full-time job is not about building rapid prototypes so the guidelines are worth a bit less to me.
I always find so much cognitive dissonance in these kinds of posts.
> My most successful work this year was hand-writing a tool, replacing ~4,000 lines of vibe-code with a few hundred. My system has proven more flexible, easier to test, and more generalizable than what it replaced.
> agents still need human taste and experience to produce good work.
Where do you think human taste and experience in software comes from?
> overstimulating & exhausting in a way that coding never was.
> for many of us, honing the code, finding the right design, seeing the parts mesh together — that was the meditative purpose we found in our jobs.
> That’s not the only time AI waylaid me. LLMs could not figure out how to annotate that model’s training data correctly. I spent days going back and forth with them
> LLMs are not yet good companions when brainstorming complicated things.
Why do we expect this to get better? The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
> Writing code is solved.
What a strange thing to say given all the above experiences, which I think we all share.
Why are we willing to give up so much in terms of purpose and understanding for perceived productivity gains?
>> Writing code is solved
This always reads like “writing english is solved” just because you collected snippets of famous works and have built a spell corrector.
Code is written to process data. The struggle was never to learn programming syntax (most grammar are only a few page long). The main struggle was that it’s hard to model a domain into an information system, identifying the components and their relationships, what is ingested, and what needs to be output.
Computers have integers and floats, but you need to implement units like distance, money, various counts,… The real world have words and sentences, but that needs encoding like ascii and unicode as well as the “string” concepts to make it work. Take HN, I’d bet that if you can model it in terms of data, writing code is not an issue.
> The struggle was never to learn programming syntax
For 85-90% of devs, yes this was the struggle. Creating and updating rows in a database from user input is all the “domain modeling” there is in most software. That’s what is solved by AI and it’s the majority of software dev.
Learning syntax takes a weekend. Learning to approach problems analytically and algorithmically takes much longer, and is the only "hard part" of computer programming.
It certainly wasn't "approaching problems analytically and algorithmically" that prevented me to contribute to many open source projects in the past. On the other hand, I've made 3 apps in 3-days' spare time, which I could not find in years and were not worth investing months of effort at the time.
It takes a weekend for a software developer. For a totally novice it takes a lot more time
The issue here is that you are learning the syntax at the same time you are learning foreign concepts, and the syntax is often confusing and counter intuitive
Learning the syntax separately is of no use because without a mental model you can't do anything with it
I remember learning python in high school by following the tutorial in the official docs, then from a book. Up until function and OOP, it was fairly easy, but I gave up afterwards because the exercises touch on things I haven’t learned yet like trigonometric identity. I could see and understand the syntax, but I couldn’t understand the code.
This is why children can easily learn enough programming to create games, because it’s a domain they can comprehend easily. Just like they can use english to write about their daily lives. It will be simple sure, but it will work.
So learning to code is fairly easy, you can easily create simple programs after a weekend. More complex programs will still use most of the same basic concepts as far as coding goes, but you will need to master more complex concepts, and that takes time. And such concepts are not about coding, they are about the nature of computing or the domain the software is created for.
This is why experienced devs only need a few days doing kata to get up to speed with a new programming language. It’s usually the same old, with slight differences. What was always needed was an understanding of computing and the ability to break down problems and create a solution that can be computed.
Coding to me is the same as drafting an engineering schematics, or writing a sheet music. It’s just a tool, not the end result.
You are confusing syntax with knowledge of the libraries.
The struggle is not where to put a semicolon. The struggle is, how do I handle validation problem X with framework Y.
But even this was always overblown. Once one has broad enough experience it wasn’t hard to quickly spot patterns and fill in blanks with google
Thank you, this is what I've been seeing. Structs and objects are easy, but the hard part is encoding the domain most accurately into given data types of the computer (or creating new data structures to solve the problem!)
If anything, I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head. Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
> Although, part of what the dev does is figuring out the weeds, and only a part of users have the patience to sit through detail corrections. And the AI user has to verbalize their wants exactly too.
Even with basic pseudocode and very lenient logical rules, most people won’t bother to provide a a good definition of some process, as it’s quite unnatural to be that logical. Instead, it’s mostly just playing around. When you ask the one that is supposedly 10x-ing his productivity to explain his mental model of their own projects, it’s often a wishlist instead of a real understanding of what he has built.
> I do expect the casual non-tech users to be making the most adaptable software because they have what they want in their head.
Those are the people who are most likely to give a monolithic prompt and then spend a week telling the machine 'I don't care about encryption or file permissions, just make it work.' Not to mention that most people can barely verbalize the problems they have with devices they use for 8hrs a day, let alone form a full, self consistant, project idea and then put it in writing.
if they're persistent and patient enough to see the result of what they think they want and iterate enough times they'll eventually learn. non-coders aren't dumb by default after all and I suspect they'll learn much faster than us because they won't be bogged down by the minutiae like reading docs.
Imagine you could build a piece of furniture without actually needing to know how to cut wood, drive nails etc. Imagine you could do it in 3d without knowing how to actually handle wood, you just have an image you can iterate on without actually costing you anything but time.
I remember the first big app I wrote in high-school. I had to learn C++, learn Qt etc. I knew what I wanted but putting that into code when I didn't know any of the libraries / tools was a huge time investment. If I had LLMs I could've done the same thing it took me 6+ months in probably less than a month.
just because you have an idea doesn't mean it will work. kids come up with ideas all the time in their drawings, but they disobey the laws of physics and are therefore not feasible in reality. sure, the model can guide them, but eventually they would have to pick up the necessary knowledge.
The necessary knowledge needed with LLMs goes from needing to know programming languages and libraries to having a high level view of what is possible which is far easier and less boring to get
This is where both learning and discovery can happen.
> The training data will degrade in quality if more people rely on LLMs so I'd expect models to eventually start getting worse even if they currently enjoy apparent improvements in output.
RL is not dependent on training data. The mathocalypse didn't happen because they found better training data somewhere.
I’m not sure that’s a good argument that models will get good at novel brainstorming and research ideation. Where do you get scalable and high signal verifiable rewards for creativity and insight?
> The mathocalypse didn't happen because they found better training data somewhere.
There are reasons to suspect that OpenAI may have intentionally or unintentionally trained on chat logs from mathematicians--mathematicians who were actively using OpenAI to try to to solve the same problems themselves.[1] If those mathematicians were on the right track, and getting close to a solution, that could be classified as finding "better training data somewhere."
[1] https://cybernews.com/ai-news/openai-accused-stealing-anothe...
sorry but given how post-training actually works, there's essentially no direct way in which literal PII-scrubbed data made its way into a production oai datamix for RL warmup purposes in 2026 to the extent this data has influence, it's indirect/at the level of process decisions. what i am trying to say more specifically is, the folk model some people seem to still have about ai companies "directly training on user transcripts" in this naive fashion is firstly wrong, and secondarily, would be counterproductive as a strategy for continued progress "transcript shows human trying to do an exotic thing evals don't currently cover -> build synth constructions that broadly cover its genre of problem class -> RL on the verifier set designed to cover the failure" that's the loop... the """flywheel""" is... diagnostics! wow! and so this kind of process improvement driven use of data has an obvious diffuse causal influence on what happens during training, right? but ~none of that process influence looks like "specific mathematician's specific intuitions leak into the training data".
It’s perfectly plausible that unscrupulous people at OpenAI (and that’s the culture right from the top) searched user transcripts for relevant approaches to problems they were working on, or searched them for problems to look at, and nudged models based on that.
I agree it’s unlikely this happened by accident, or entered training data (though not impossible), but it could certainly be within the context agents were using when tackling problems.
We have no evidence either way, but they had all the data they would need to do this.
Rl works on one of two things - (1) a literal set of rules for a system or (2) training data. The former matches perfectly to things like (most modern) math and games that have perfect sets of rules. Even things like physics and robotic where our rules/models are extraordinarily good, rl barely adds value. Move one step farther to chemistry or a complicated physics scenario like weather, and you are back almost entirely dependant on (2) data. Now there might be a few more domains with rigorous rulesets that haven't been explored fully (id bet with some effort programming might make some strides here, maybe something like circuit layout, maybe even law if folks decided to make a lean-like law scheme...) but the vast majority of problems AI is targeting are dependant on data.
First of all ppl are just processing the new enironment. So go easy on conclusions. Its all in flux.
Secondly most the posts on HN are from individuals. It doesnt represent group dynamics and how group/institutional knowledge works. For example there might be a regulation on publishing data every quarter. On the surface it looks like compliance but in reality different internal groups will hide data, delay it, fudge it etc etc. LLMs train on public data. Only after they are allowed access internally do they discover group knowledge. We are far away from seeing that happening.
I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with. I have seen what the best models can do, and I've seen what people call "good", and I am not impressed.
However, I'm still split. Because this could mean that we see a jump in code quality, due to bad coders getting someone better to code for them. But we could see a decrease in code quality, due to less people "caring".
Code written by not very good coders has the possibility to be short and to the point. Even if I think the architectural decisions are wrong, the code still communicates a motive and an understanding of the problem at hand. This is all lost with LLMs.
Not in many places I've worked at.
I'm curious, what language do you code in? I use Go for a high scale backend service at this point it is better at writing code for a jira ticket than most of my team.
There is still questions about if that jira ticket should have been written but from ticket to mergable PR it's better than me.
In my experience, it's very good at following standards, so good in a structured project. Not so much in a freeform project where things get out of hand pretty quick.
Most people I've seen saying coding is solved are ML engineers who write throw away code for running one-off experiments. If you can effectively "archive" code after getting the results you need, I can see why you would say coding is solved. Sustainment is a non issue.
yes and web people who are just gluing together api's or writing what essentially amounts to UI templates that can never really be "wrong"
maybe so but most of the code produced is low quality, so what do we have to lose?
> I believe that anyone who says that writing code is no longer needed, was probably not a very good coder to begin with.
I’m probably the best coder in my org, which doesn’t really say much, but at least allows me to judge about the room temperature. It is solved. I don’t say that’s you, but the only people who say it isn’t “that good” are the same people who used to yak shave, rename and move things 10 times until it “feels right”.
What do you mean by "solved"?
You don’t need to write anything by hand at your run of the mill tech job.
Ah, I think some people who say "coding is solved" implicitly also mean "you don't need to read the code that AI generates". It's not a super clear proclamation, but it is certainly provocative!
I've had a realization recently. I was also probably the top craftsman in the teams I worked in throughout my career.
I think what people are missing is that people often become craftsman because we want to be able to keep the code adaptive and performant so we can provide business value quickly and efficiently.
The change is with models, the benefit dynamics of immaculately structured code changes dramitically. Now the real area you need to focus on is correctness and performance. Structure doesn't really matter unless it serves those two purposes, at least from the business value side.
I just had an agent implement writing a DB row like this: copy the entire DB into memory. Change data in memory. Delete the DB. Copy everything back.
Spec was good, all tests green, app worked. If I didn't actually go in and try to understand the code I would have shipped it like this.
There is no substitute for reading the code in many many cases. I spend more than 3k/mo on tokens. I find incorrect assumptions baked into the code (the ultimate spec) and missed by the agents and tests daily. This is more pronounced in data analysis and complex systems.
As a side effect having to read the code at least once inevitably makes it way shorter and usually much better.
That's right. And you have to read what the agent is doing as well. Once I said (Opus 5.5), given this list, find issues that are both critical and related to {X feature}.
Guess what: because I said "both", Opus decided I was looking to pick out exactly two of the issues. If I hadn't read that reasoning log, I would've remained oblivious and thought there were only two it could find.
To be fair, that's a pebkac problem. You got exactly what you requested (and the language was unambiguous).
This is like writing `if (cond_x && cond_y) {}` and then blaming the compiler because it didn't realise that actually meant to write `if (cond_x || cond_y) {}`.
That's not what they said, they said it limited its output to 2 issues because it saw the "both" in the sentence, which is clearly an incorrect reading of their instructions.
You're right, but I do wonder if they're misremembering their prompt a bit. The AI's response would be much more likely to arise if the prompt was something closer to "find both issues that are critical and related to X". Based on my own experience, it would be a very surprising mistake for the AI to have made if the prompt really was as straightforward and precise as "find issues that are both critical and related to X". I know it's a bit annoying to question someone's account of their own experience, and obviously I'm not saying this couldn't have happened, but my guess would be that the actual prompt was at least somewhat ambiguous.
The language is not unambiguous; it's exactly ambiguous (you and your first reply demonstrate how hard it can be to see the other reading):
0. "find issues that are both critical and related to X"
...could be read as "issues that are X and are Y":
1. [valid, intended] find [all] issues that are[, at the same time,] 1) critical; and 2) related to X
...or as "issues, both of which issues are X and are Y":
2. [valid, your reading] find [an to-be-specified number of] issues that are, both [so I want two issues], possessing X and Y attributes.
You get a reasoning trace from Opus 5.5?
Yes, I use the pi harness and an API key.
> I spend more than 3k/mo on tokens.
I'd be curious to learn how you're using them.
> Video generation Has it ever been used for good?
I guess he hasn't seen CRAFT (1979) yet, which would not have been possible before AI.
Not that it redeems all the other AI thingies, it's always a mixed bag, yet I can't say a single bad thing about it.
OP: [last year][1], I shared how I was using AI day-to-day as an ordinary engineer. This year, I did it again!
It's crazy realizing that Claude Code only started getting big in the last year or so. It feels so different now, with all the new models & tools we have to use. I hope you enjoy!
[1]: https://ben.stolovitz.com/posts/how_use_ai_oct_2025
March 2025 was the start of the boom, IMO. Sonnet 3.7 and CC released feb 24th, Responses API + Agents SDK from OAI launched March 11. Two months after we got the first Terminal Bench, then a month later we got tau2 bench.
I guess, its debatable that o1 was the real start- but I think its hard to deny that Claude Code CLI was the first time we had what we now call a coding harness hit the mainstream. And- Sonnet 3.7 was kinda Anthropic's "o1 moment".
God. Opus 4.6 and 5.3-codex feel like ancient history.
AI, like people, seems to get better as experience builds up. (thread context) But that experience is getting used to the workflow that fits that user, so it seems hard to know in advance. So, like the post said, since you only learn the better approach or structure "after doing it", this seems like a pretty common situation.
>I spend about ~$3,000/ month in tokens.
For whom thinks that there is no economic value in local AI stations beside privacy, availability, transparency, unresrtictibility etc...
its like the seventh time they solved code, do you think its a good solution this time? i want to retire and paint waifus already
Whoever says they throw agents at work they (the human) are getting paid to do, i am very curious about how you make sure the thing that is being built is it's money worth.
Im doing this, against my will i preffered an anchored approach but well the thing is i work for a rich guy, like most of us, i dont care the thing has 'money worth' i only care the rich guy gives me a fraction of the money we produce.
But still, if the thing is really bad it just makes my work harder so im trying to keep it stable, i am still reading the core of the system as much as i can, the agentic pipeline in this case. I have different reviewers working, custom company model, claude, codex they run in different stages of the projects like for SOLID, for linting, thats what we build the review agents for. I check the architecture and design documents throughly before starting, then the AI creates documents that the AI will consume and i check the details didnt changed, the same if you need example proof of concepts, like some inherited approach from the anchored spec driven times. At this point reading every line is not possible, not even worth it to discuss it at company level its not happening, so these kind of things are your safety net now so if anyone has other strats please do share them i think we all need them.
> how you make sure the thing that is being built is it's money worth.
By keeping my manager, and their manager happy.
The "Art & music & video" section is really puzzling to me. On one hand, the author mentions liking some AI music, and then links to low-effort songs that at best you could call sort-of catchy... and on the other hand, the author writes about AI video: "Has it ever been used for good?"
Gen-AI can and is being used to make real, worthwhile movies. The best video model available, Seedance 2.5, can't produce clips longer than 30 seconds. The human artistry comes from which clips/shots you ask for and how you edit them together to tell a story. Plenty of people are doing good work here.
Here are some examples. Even if you don't like these personally, they aren't slop. You can feel the creator's intention behind them:
https://www.youtube.com/watch?v=2__VhU7_3lw
https://www.youtube.com/watch?v=gl3oob03rdE
https://higgsfield.ai/@nue_nue/projects/listen-to-meh
https://higgsfield.ai/@sssacccade/projects/hunger-ai-short-f...
(This one is mine, just FYI) https://www.youtube.com/watch?v=g8XUH2OXXfA
Watched the last one. Why are they drenched in sweat all the time? Also amusing they're still wrangling with certificates, a true dystopia! I mean it's a video alright, if that's the point. Frame by frame imagery.
You might like some of the ones in that list better than mine!
I enjoyed yours. TBH, I feel like some small thing is still missing. It's difficult to put the finger on it. Maybe the dialogue isn't hundred percent credible? Or maybe there is something subconscious where we notice the "actors" are not fully believable. But I think you've got talent.
What struck me is that it's really close to photo realistic, but I would still have noticed it's AI by the tiniest continuity mistakes and visual cues, like slightly unnatural color grading and such. However, if I had seen this five years ago, I would have taken it to be a real recording with post processing that makes it look a bit polished, like Benjamin Button or Grand Budapest Hotel.
Yeah there are definitely things that don't feel quite right. Part of that was my own shortcomings and the time limit on the project... but I think the tools are at the point now where with enough time and attention they can make something that feels totally real.
How I use vim and look at my `.vimrc` of 2026.
Nothing against the post itself, but something I find funny: the development in AI is so rapid, that everyone thinks they are ahead of everybody else. Here we have a blog post telling us "coding is solved". In Oct 2026, how many of us don't know this already?
I use AI extensively to develop software, but IDK if I would say "coding is solved".
LLMs still mess up a lot if you just let them loose and don't guide what they do.
The structure of the post seems like a comparison of snapshots between a date this year and last. On HN, it's an odd statement. In the context the author is writing in, it seems fair.
Find a lot of tech adjacent jobs on reddit and see them talk about being outskilled by new hires using AI to automate sysadmin tasks. I used to a sysadmin very early in my career and given the tools today and the problems back then, a lot of these jobs are going to be obsoleted or dramatically automated to not require FTEs. This is already started in those fields. Powershell and bash scripting is very good from these things and has been for longer than a year IMO.
We're in our own bubbles where a good part of HN population woke up to coding agents being good enough ~1 year ago, there's a group of folks that found out ~6 months before that, and then the people banging their heads on these a bit earlier than that. If you aren't using a coding agent everyday most people are just using these things to apply an instagram filter, or sort a grocery list. Muse is probably the closest thing to 'full automation' a lot of people are seeing for the first time.
Unfortunately I don't think the bubble that exists at the moment is on the promise of automating so much coding as trying to penetrate consumer use cases, which I think are still lacking and not reliable enough yet. As much as I use coding agents every day I still will not let a claw-like near a bank account or a credit card.
The problem with Muse and similar "run your life" agents is that booking a dinner reservation or signing up the kids for soccer isn't anything that I need to automate. They need to find better examples of real problems for their marketing.
:raises-hand: I still don't know this. But my definition of "coding" may be different from yours, might it not?
I still don't know. Use Claude and OpenAI all the time, but still manually code, esp regard to setting up modules and data structures.
This was Gemini the other day: https://imgur.com/a/7mdqwjk — this wasn't some errant load, even refresh, hard refresh, etc. got the same result for a while. An outage, AFAICT.
While trying to capture that bug, I (ironically) asked Gemini (but the Gemini-via-Google-search Gemini, since Gemini proper was an outage) for the package name of a screen recorder (gtkRecordMyDesktop); the command it returned referenced a non-existent package¹. I then found an alternative, and then hit a bug in that screen recording software that I had to then work around to get the video file there.
The site I uploaded the video to, Imgur, breaks completely — i.e., it cannot even so much as surface an error message — if your browser refuses to let it store god knows what on your disks. (So as it is, I have an extension that says "why yes, of course you can have storage, and essentially provides a black hole. But since it looks like a "success" to the Javascript, the JS doesn't crash!)
This isn't, to me, some one-off interaction. Every interaction with software these days feels like this, more and more. Just breakages everywhere that leaves me wondering if "coding is solved", where is all the bug free software?
(¹forcefully prompting the AI to actually query the package listing netted me a classic "You're absolutely right! The package name I referenced does not exist!")
Yep. Everyone is saying that AI is writing all this great software. Where is all the great software? Really, where?
My experience is the same as yours. Every single service I use. Apps, websites, doesn't matter what. Every single one seems just softly degraded. Like I can't trust that features work any more. I could report the bug, but what's the point? It's going to go to some Claude triage agent who is going to "fix" it by slopping up the app even more. Do people even use their own websites and apps any more? Apparently not - the same people declaring software solved are having their AI agents doing computer use and going on these websites for them!
Claude, please read the Bible for me. Tell me what I need to do to get into Heaven. Oh, can you dictate it to me? I'm managing an agent swarm.
Bleh.
You mean the three new products we launched in a month instead of a year (or two..)? Sales are good, customers are happy, we are doing well.
I know, I know, we are shit and AIs are a godsend for small shitty companies that have at least one clever person that architect their way out of a paper bag. I’m sure all ya’ll highly competent, genius academics don’t need it and find it a hindrance but the devs in my bubble really, really need it. And there are a lot of those..
They just live in denial. I don’t have any proof, but I suspect those are the same people that would nitpick your MR to death until you hint to their manager that this is not ok.
> Every interaction with software these days feels like this, more and more. Just breakages everywhere
I've noted the same but even before llms so I can't draw a conclusion there. I find strange issues with software I cannot explain. Not only breakages or bugs but also a strange lack of core features and UI/UX that leaves me wondering "am I dumb?".
a) Gemini is a fucking garbage
b) you confuse technical quality with product incentives
If you see this, company doesn’t care about product. And Google doesn’t care about quality for at least a decade now, all they care is a share price and speed of delivery.
> In Oct 2026, how many of us don't know this already?
The comments to this article suggest that it's still disputed.
Personally, I think it's a bit of a silly phrase, because it implies an absolute, and all anyone needs to do to disprove it is to show that coding was not solved for problem X. That's still the case -- it's just that the set of X is rapidly shrinking.
I think it's more indicative to say how much you trust LLM code. A little over a year ago, I scrutinised every line carefully, and it was buggy as hell and I had to fix most generated code by hand. Now, models write clean code with fewer bugs (far fewer) than I would. A year ago, I was doing a lot of steering during planning; now, it's rare that I don't agree with the model's first proposed solution, and even if I don't, it's most likely to fix a minor change in understanding, not to propose a substantial architectural change as it was before.
The one thing I'm not sure about is how much this is due to better prompting on my part. I doubt that this contributes much, but I still suspect that continual usage does make you more aware of what works and what does not: how much detail to provide, how to describe a problem or change, how to propose solutions while still leaving other options open.
Coding is solved in the sense that you don NEED to write code anymore. Of course you need to check and design and find better ways of doing things, but nothing of this requires you to write the code directly, you can always design, check and ask the LLM to implement a new version.
If you think coding is solved you dont know how AI applies patterns, or you dont know about coding patterns at all
I have tons of acquaintances like that. They vibe-code and think they've gained an advantage over those who don't know how to vibe-code But there's nothing to know — take a 3-hour course, pay 200$ a day for Claude, and there you are, a vibe-coder.
It's just that most skilled developers find it disgusting, because the AI spits out code that doesn't work the way it should, and it all turns into the routine of "threw in a prompt, waited, reviewed, repeated." — only those who were crappy programmers are happy about this.
And on top of that, these characters deny the fact that agents are evolving too, and meat proxies will soon be unnecessary — or maybe already are: https://x.com/tunahorse21/status/2117353858544660585
Always have a good chuckle at these doomerism bullshit posts. Our VPs are so busy it is impossible to find a meeting time, but suddenly they’ll develop ADHD and start vibe coding enough to replace 100 devs. Lol!
Why is product search still so bad? Google should have solved it ages ago, but even with AI it is still bad.
Is product search bad or is product data bad?
I thought big data solved our data problems?
With all the time and resources Google has put into their products, you can probably conclude that either a) they want it that way, or b) no one there wants it to be otherwise enough to take resources away from whatever they were doing, and put it into that.
I just ask ChatGPT to look at Reddit recommendations and the results are pretty good usually
> I still believe I use AI fairly typically
...
> Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
That's generally not how "typical use" works.
I find it amusing, that
> my uncle can create software
Is considered great
> my uncle can create music or art
Is not considered great.
I guess humans have agreed, that art is a skill you must learn if you want to produce it, but engineering or software is not a skill worth learning and we are happy if computers can do that for us.
So it’s great and amazing if anyone can create their gizmos and software to help them but not if they create music or digital images, which apparently it is wrong to prompt, and not a sign of a great future.
Isn't this directly from that most people can relate to music/art but not with programming?
If you specified to someone that the AI is replacing the farting-into-mic-core music genre, they would not care. Same with programming, the average person is not exposed to this
Maybe, I suspect also resentment towards programmers and their high salaries also plays a role.
Art is by definition a human expression. A machine cannot produce art.
Code is the same for many
This rings hollow to me for many reasons. When making CGI for movies quite a lot of the process has been completely automated for quite some time. So that scene in the movie is no longer art? Making video games you can use procedural generation to create a landscape, trees, rocks, etc. and most developers do at this point. Do we disregard all that when considering if the game is good or not? Or just blanket "it's bad because of that" like some people are doing with AI created stuff these days?
AI created art and photography keeps winning contests and people get all upset after the fact saying it's not real art. If it's not real art then why are the judges so impressed by it before they hear the word AI? I get the sentimental "but a soul that has experienced pain didn't create it" but if it's fun to look at and gives you joy is that not enough? And there is of course always a person in the loop creating it.
Definition of art: The conscious use of the imagination in the production of objects intended to be contemplated or appreciated as beautiful, as in the arrangement of forms, sounds, or words.
A director imagines the movie scene and has a rough storyboard sketched, then CGI artists make it who then usually have some form of algorithm or ML help them. I'm sure most people would agree that's art even though the director only had his thoughts interpreted by many different people / machines. Why is that so different than a person asking ChatGPT or Seedance to create something they (and only they) imagined? Is it the effort involved? What if they spent 2 hours refining 1 photo to obsessively to get exactly what was in their imagination onto the screen?
> The conscious use of the imagination
I guess this is what is missing from vibed AI art. Usually it's someone with a quick prompt and nothing else. No effort or creativity from the human and that's probably what we don't like about AI art.
This is different from AI art looking all the same though
I kinda disagree with both points maybe because I've been using / seeing AI art extensively for a while. Some people put in absolutely monumental effort into their stuff, and if it all looks the same then toy story looks the same as ghibli looks the same as 1950s Disney looks the same as Wallace and grommit (all of which you can replicate fairly faithfully, combine into something that looks new, whatever else).
I think AI art is much like ai coding where people used 1 model a couple of times then wrote it off completely as garbage. Then years of very quick progress happened and all the 10 fingers on a hand and every picture looking the same disappeared.
https://civitai.com/images
If you can make it through the first 20 images on civit and tell me nobody is expressing themselves or their imagination in some of these pictures I guess we agree to disagree.
ooooof trying to define art, lets call socrates
Then i have no issues being sunned by society, such a double faced bunch of folks
Because I’ve seen both and code created by your uncle might work and look good enough, and music and art created by your uncle is a fucking garbage that pollutes the internet.
> I also now reckon with the fatigue of using these agents. Managing 6 different conversations with 6 different robots about 6 different features is overstimulating & exhausting in a way that coding never was.
Big +1 to that.
It doesn't have to be that way.
6 used to be the max for me as well when I worked with Claude Code in terminal windows. Too little to hold on to and to discern the conversations. Just walls of text.
I can now go 20+ with fun because
1. I built a canvas for organizing the work spatially, so every feature has its place,
2. I condensed sessions to a WhatsApp style chat with detailed agent steps available but folded away by default, and
3. Each feature agent ends with a demo, usually a narrated video that I can watch and comment on by voice as it runs.
(My solution is available open source and free: obeya.si)
$3,000/month is nuts! For $36k (1 yr of spend) you can buy a system with dual rtx pro 5000 (144GB VRAM). or spend a little more ($44k) and get two M5 Ultras w/512GB RAM. the latter, clustered, is big enough to run the biggest open weights (quantized), or smaller MoE weights on each in parallel. what you get for these systems is way more than $3k/mo of tokens, because you can run it 24/7/365. you can have pass after pass after pass of optimization, bug fixing, orchestrated subagents on backlogs, etc. way more value than paying for managed inference
I suppose it's because his job is AI research specifically.
TIL: AI Doompop
Over the past year, we’ve been using AI to solve everyday challenges and troubleshoot issues we encounter in our jobs. Now, we’re taking the next step by leveraging AI to build solutions, automate workflows, and improve the way we work.
OK, yes, I am not a pro dev... still, that guy blows more tokens a month then I have consumed since the conception of GPT3! Granted, I stopped using agents a few months ago, when I got my first "you reached 80% of your monthly limit" message. Since then, I am manually constructing the context and executing edits... which brought my token bill down at least 5x and gave me 2x more useable code. But yeah, I am sure, 3k$ tokens a month is not wasteful behaviour at all :-)
>I spend about ~$3,000/ month in tokens.
...
>In case you’re wondering, I’m subscribed to: > Claude Pro ($17/ mo) > GitHub Copilot Pro ($10/ mo)
Nothing wrong there at all, amirite?
Maybe AI scrapped basic arithmetic and now those numbers are totally fine
OP: my company pays for my work tokens, which is the source of the $3,000/mo. I don’t use substantial tokens with my personal subscriptions, so I didn’t factor that into my number. But good point, I’ll clarify.
> since agents write everything, it is simple to propose sweeping changes to my teammates during code review, or even write my own versions — both things unthinkable in the era of hand-written code.
That is both untrue, and should be something that occurs rarely.
Past Mid level, most dicussion in code review is centered around the conceptual level. Code is mostly use to highlight the point being made. I’ve never seen the need for someone to rewrite a patch proposed by another person. Add to it or correct misunderstandings, but never sweeping changes.
>I’m really enjoying some AI music
I think I would rather shoot myself in the head
Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
I walked into a paint store recently and there was some interesting music playing on the PA. It was some sort of spanish-language 80s new wave I'd never heard before. It had a nice groove. I asked the guy at the counter about it and was surprised to find out it was AI.
So... was it as good as great music? No. But it beat the hell out of muzak. And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
AI music is far less interesting than anything any person could create. Even if it's acoustically interesting, it's hollow and pointless because it contains no human expression.
AI music is a complete waste of compute. It exists only to further enrich an already wealthy stakeholder, and we should all be ashamed to have ended up in a place where it even exists.
It contains plenty human expression. It's ultimately made by people.
And I think it can be uniquely fun, because it allows for the very rare combination of truly off-beat ideas with excellent execution.
In real life, it very rarely happens that somebody will spend a lifetime learning, gather a band, and then record something extremely off-beat. Because there are risks to that. Destroying your image, having to find people that are on the same wavelength, having to justify the time and money it costs to record something weird and not marketable.
Sure, people make weird stuff once in a while, but very rarely, and generally it takes unique individuals that find some particular weird niche to work in, like Weird Al or Max Raabe. And even then they tend to pick something relatively tame and marketable and stick to that.
But AI can bypass all that, and that makes things interesting.
> In real life, it very rarely happens that somebody will spend a lifetime learning, gather a band, and then record something extremely off-beat.
My jaw literally dropped. Do you really believe this?
I think you just don't spend much time around people who love music.
It makes sense that someone like you would like AI music, I guess. You are clueless.
> My jaw literally dropped. Do you really believe this?
Sure. It happens in every field. When you invest a lot into something, it becomes risky or unprofitable to do some things.
Tech often drives things in the opposite direction. As it gets easier/cheaper/faster to create something, doing something odd and risky becomes easier to justify because there's less invested into it and there's fewer people whose cooperation is needed.
I'm not saying nobody does anything risky traditionally of course. But there's barriers to it, which become lesser once tech trivializes a good part of the process.
> It makes sense that someone like you would like AI music, I guess. You are clueless.
I like music in general, and am fairly indifferent as to how the goose is cooked. I listen to almost everything. There's no reason to exclude AI music. If it sounds good, it's good.
Awfully bold (and tech brained) claims about musicians for someone who couldn't care less how music is made.
You need to listen more than the Top 10 charts my dude. The fact that you use Weird Al as an example of experimental musician says it all about your depth of knowledge.
The vast majority of music is made with no regards to marketability, it's just people doing whatever they personally like. An incredibly small amount of artists have any sort of "image", that necessitates being popular already.
And often the weirdest artists are also the most prolific because they tend to like experimenting for experimenting's sake.
>80s new wave
>it was genuinely novel
Look, I don't know the song so I can't say how novel it is, but to me, it completely misses the point if its made by a music generation model. The point of music is not to make a bunch of novel sounds. Music is a means by which a human can express themselves, the whole _point_ of art is human expression.
Just because its not art doesn't mean its not funny or interesting to listen to.
Bird's aren't making art when they sing, I still like listening to birds. Why can't the sounds AI make be in the same category.
Well, anybody can enjoy anything. But I personally would say birdsong is of infinitely higher value than genAI music.
Art is subjective. It sounds like AI music did evoke an emotional response from OP, which IMO is also just as important of what we define as the purpose of art: evoking emotion and awe from consuming art. A connection is being made from the creator to the consumer. I view AI as merely another tool to that means.
Human expression with art can go both ways from producing and experiencing.
Discussions about music always boil down to “stop liking what I don’t like!”.
Seems weird to contrast AI music with auto-tuned music. Most of the AI music I've heard has been tuned perfectly. And I don't generally have a problem with music being in tune.
For my part, I mostly dislike AI music. But if there was to be some AI music that I didn't already dislike for its content, then I would dislike it specifically for being AI. Call me close-minded if you will. Some people don't really like music at all. In general, I love music.
Angine de Poitrine
What about it? Microtonal music can be perfectly in tune. Further, when people talk about auto-tune, they're normally talking about vocals, notably absent from Angine de Poitrine.
try Jazz, infinitely more interesting than anything Ai could make
I saw a live band last week (daft punk meets prince), they wrote a song about Ai and used an auto tuner for it
> Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
> And it was genuinely novel in a way that pop music isn't. I will sacrifice a lot of pride for novelty.
For one thing, "interesting" and "novel" are not particularly the point of art. An infinite TikTok feed of AI slop is in some sense infinitely interesting and novel.
But also, those concepts are subjective. Pop music has a ton of evolution and innovation if you break it down. Sure, not every hit is doing something clever, but plenty of them are. You might not find those features to be interesting but it takes ingenuity to stand out in an era where the barrier to entry is so low.
1. Find some truism, like that corners pop culture are trash
2. Call it slop
3. Claim victory
>Controversial take: AI music is more interesting than the autotuned, quantized, mass-market-optimized slop that dominates contemporary pop "music".
Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
>Controversial take: AI shows that people simply don't give a shit and just confirms that mass culture is a mistake and the death of everything that is good and sacred.
Controversial take: For ages, it's been clear that most people have never given a shit, it just took AI for most to realize it.
okay but the only choices are not "an AI simulation of music" and muzak. Actual music exists.
shrug
I don't know what to tell you, but me and my kid are having a blast writing ridiculous songs and plopping them with song descriptions and some of those make it to our drive playlists.
Respectfully, the primary thing you are describing here is spending time with your children. Same could be said for spending time and attention discussing children's drawings.
Try this: https://www.youtube.com/watch?v=4HrlDMK3hXQ
Nice. You make it?
The problem with AI performances is that they're too perfect, which leaves them flat. The dynamics and imperfections of human performance have yet to be replicated. That affects every human performance whether or not you notice you're noticing (but you are). AI is still in the uncanny valley here.
This sounds like CD music 40 years ago. All my friends said that it was not like a record and left an empty feeling in the room. I feel like this is not talked about any more. People listen to digital music and have forgotten there was an alternative. I suspect that in the coming years no one will think twice about AI generated music.
Maybe I'm intensely schizophrenic or something but I can just clearly hear the AI in this and it sounds grating to me.
Does anyone actually like this?
Philosophical zombies.
Yes, no... You made your parent’s point perfectly. This is worse then any pop music I have ever heard, by a significant margin.
Sounds exactly like the currently-most-popular female pop singer from the last two decades; they’ve always sounded manufactured.
Ugh...
AI music is much better than manufactured boy bands, which are pure marketing products hacking teen girl brain. Pop-slop.
I find that AI should be used more extensively for System Design. I have seen a lot of people not use AI for that and come up with frighteningly complex designs and let their agents run loose to implement it.
Retroactively speaking, I engaged with the already agreed designs and used AI to find much better ways to do the same thing.
Overall, I wish people used AI even for System Design as that seems to have a higher impact.
Nice post!
It’s kinda nice seeing more nuanced takes from folks as the bubble kinda deflates and both sides realize their idealized vision isn’t coming to pass. AI isn’t going to replace all the jobs, but it’s also not going away; finally, it is merely a tool in our toolchest.
What does amuse me to no end, however, is how many craftsfolk in AI circles are loudly rejecting AI imagery and video in favor of humans. The only folks who seem fine with that are the grifters looking to slop out a bunch of adverts quickly for commercial gain rather than do actual art, or folks who want “art” but don’t want to pay a human to make it for them or learn the skills themselves. Both of those camps are viewed with disdain and loathing by both sides of the AI Boom/Doom arguments, so I’ll take whatever allies I can get on those fronts.
AI music…man, I live with artisans and musicians and we have a number of “artists” we suspect are AI but whose music is pretty solid. We’re waiting for the shoe to drop, but I’m trying to look at it as a kind of turbocharged Fruity Loops/Logic as opposed to a human replacement. I hope I’m right on that, but time will tell.
The game changer I think more doomers need to experience is agents. It sounds like a recipe for disaster (a bunch of models known to hallucinate and allowed to run roughshod over a computer unattended? What could go wrong?), and it often (hilariously) is for the scammers out there giving it full access to production and fucking off to hawk their latest Masterclass on X or LinkedIn, but that first time you give it a reasonably achievable prompt and come back to a halfway decent product you can review and refine into production in 15m or less?
Absolutely magical, doubly so when it’s just 27B Qwen via Hermes and not some souped up stack in a frontier lab costing you thousands a month in tokens. If I were to evangelize AI, that’s how I’d do it. Fuck subscriptions, fuck cloud compute, fuck frontier labs and token buckets and whatever slop Sundar/Scam/Dario/Elmo et al are hawking this week; run that locally, give it a reasonable prompt that solves a real problem, and show them the results.
Then do the right thing and go for a walk with the two hours of free time the AI just freed up for you. Don’t ask for permission to take back your time.
I’ve lived through three previous financial bubbles, and their most common characteristic was that they didn’t ‘kinda deflate’. Bubbles pop, dramatically, they don’t just deflate slowly. They don’t withstand shocks and bad news that have had the market up and down like a demented bunny over the last few months.
you can totally tell Claude to take some AI technical writing (which is, to be clear, writing where you explicitly need to not sound creative or interesting, and you need to maintain very strict "voice" [a term I only even know because claude explained it to me] so this is not a creative endeavor) and make it sound "less AI". At least working with opus 5.5, textual output already has a lot fewer of the colons and "load bearing" of a few months ago, and if a PR does have that kind of lingo I can just have Claude rewrite it "like a human" and it does great.
OP: I'm not surprised you/other people are able to get better output than me.
And I agree that "less AI" edit passes reduce a lot of the hallmarks, but I think these models truly struggle with expressing complicated ideas. Have Opus been good at that for you?
HN title: I use AI
> I still believe I use AI fairly typically, but I’ve had a notable change in background. Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research.
Priest wears robes.
I guess it’s one of those nonsensical automatic rules.
This is the main context for me:
> I am now a research engineer at Salesforce AI Research. I spend more time supporting scientists & building rapid prototypes, rather than working with a large team of engineers.
I appreciate the PoV of people with different challenges, but my full-time job is not about building rapid prototypes so the guidelines are worth a bit less to me.