Nope. That would require the people paying for the code (those requesting the projects) to see something wrong with the code. As a coder, you would look in an see a mess. As a user they'll probably see something relatively nice, with some maybe strange behavior and non ideal, but what software sin't non ideal? It does most of what we wanted, and you finished it how fast!?
Not at any decently sized company. Delivering features is all that matters. By the time you're cleaning up the buggy mess, they were promoted two times and work somewhere else now. If anything, AI makes it much easier to suck and still get things done to an acceptable degree.
It is funny that only real research on productivity gains from AI shows at best very minimal gains, but AI bros will always tell you "no no no, you have used wrong model, try a different one, there are more of them, you have to try, trust me" and call that a "research".
Ah yes I'm an AI bro because I said the author must do thorough testing before coming to a conclusion about LLM's.
Looking forward to the authors next blog post on how after driving one car they find that all cars are slow and uncomfortable. Then the follow up after trying one phone and that all phones have bad cameras and battery life.
DeepSeek has been the most used model on open router by far, only temporarily overtaken by ox alpha when it was free, so it's the most representative experience
> Why not use actual frontier models, and you know do some real research, before writing a blog post?
Seems like they've already done more research than you. Their results not matching your expectations does not indicate a lack of research. If you feel they missed something, why don't you go spend your own money on whatever frontier model you want and go publish a post with your own conclusions.
What expectations do I have? They used one AI model and then wrote a blog post as if they have an informed opinion. It has no credibility whatsoever, why would I listen to anyone's opinion who hasn't done proper research.
It's like playing one video game and then claiming all video games are boring and have bad gameplay and stories.
There are some good points, and I ask the question of where is the ground breaking stuff myself, but severely weakened by
* stretching the timeline: the actual real programming ability appeared in LLMs in the last 6-8 months, not 3-4 years,
* using the weakest possible tool: and I bought $10 worth of DeepSeek credits that is a far cry from Claude with Fable.
Also, I know nothing about marathons but for most uses putting the app, database, and background processes on the same server is very much the right starting point. With the next steps being employing Cloudflare or similar solutions long before managing a fleet of servers.
I remember being told in early 2025 that only now has "real programming ability" appeared in LLMs.
And since then there's been 3 more "now real programming ability has been made available and previous stuff was just toy examples" cycles (summer 2025, winter 2025 and spring 2026)
Looking forward to the next "everything before this was trivial and bad, here's the good stuff" moment
Yeah, it's interesting how the current thing is always the end of programming, but then when a new thing comes it turns into absolute trash that can't be used for anything.
If you haven't been using the latest NanoQwen27B-5.2.10-Turbo-KimiGLM-Pro-10.4 in the past 13 hours, your information on what LLMs can do is outdated and you're falling behind. Check your priors.
It's a bit Goomba Fallacy. For different people the threshold was crossed at different times. That you're getting told different things by different people doesn't make those statements self-contradictory.
Even as someone using AI on the regular I'm starting to hate the "You didn't actually use this exact most expensive model so your point is invalid" argument.
This is fair to say if someones last experience with AI was copy-pasting code into GPT3 chat windows years ago, but Deepseek is a more than capabale model and enough for someone to get an informed opinion about the technology.
If people have actual counter argument, use those. And if some of those counter argument are "What you say isn't possible, the neweste model can do and here are examples of that", that is fine.
But a blanket "Nuh-uh, it wasn't Model X" is not only a poor argument but also automatically invalidates any criticism when a new, better model comes out - and that can't be the basis of a good argument.
In this case, the argument is valid though. Only recently have some of the models become powerful enough (for some) to actually be useful in day to day programming, without too much hand-holding. For most, this change occurred with the introduction of Claude Opus, and OpenAI and Google have caught up. Unfortunately, none of the "open" models is at this level yet.
The field is moving fast, and asking for scientific arguments is not realistic. It takes an extreme amount of effort to show what exactly is different.
We were in a similar position with static vs dynamic typing for decades. There is still no scientific proof that one is better than the other, but it is quite obvious to professionals which flavor works better in a given situation.
So, even though the argument might be sloppy, I subscribe to it. Using DeepSeek to dismiss better models is the bad argument here.
Edit: added "(for some)" as a disclaimer that you still need to be a fairly decent programmer to actually benefit.
> Only recently have some of the models become powerful enough (for some) to actually be useful in day to day programming, without too much hand-holding.
That still is very much not the case. Every model, yes, including $most_recent_model, needs supervision or you will get burned.
The models got good starting with 2026 and that isn't some attempt at excusing it. Companies like OpenAI started building dedicated models around their coding harness called Codex, there was gpt-5.3-codex and it was both cheaper and better at using the harness than the regular models. Then they started merging the two model types into their main release models. All of this happened like 6 months ago.
You don't have to pay money to use Codex, there is a very generous free tier that costs you nothing, you just have to accept being told you're out of tokens every day. Because your token limits are low, you need to make sure that you accept or reject everything manually and when it tells you that it wants to run a command you have to paste in the command into your terminal and only paste the relevant output back otherwise it floods the context window.
> Even as someone using AI on the regular I'm starting to hate the "You didn't actually use this exact most expensive model so your point is invalid" argument.
What the author of the article is doing is dismissing a technology so disruptive that it's basically all everyone's talking about in the "tech space" at the moment (I mean look at HN frontpage for the past few months), by trying a relatively mediocre (but still quite good) model for about 10 seconds.
The reality is that frontier models suddenly got very good in the past 3-6 months. It has it's problems, and you need to learn how to use this new tool (as with any tool).
But models can and do generate good code. They also can and do generate absolute garbage (even Fable).
You need a good harness, tools to help the model check it's own output, good context, and a good idea of what you actually want. If you have those 4 things, the chances of generating absolute garbage are pretty slim (but yeah, still there).
“I wanted to see if I can get in on this 10x magic. I decided to put my money where my mouth was and I bought $10 worth of DeepSeek credits to use with a project I was working on.“
I'm sure DeepSeek isn't the point here. You can change the name to whatever you prefer and the article still holds.
Actually, I think the author put DeepSeek on purpose to avoid the obvious ChatGPT/Claude comparison — because whatever he chose, there would be a question of why model A and not B, while the point of the article isn't about models comparison at all.
It is the point. Also open source model enthusiast tell you otherwise, there is a coding quality gap between these models. If I use DeepSeek, I do so knowing that I have to limit to simpler tasks on smaller, well specified prompts. What the author did, letting the model do the planning, is not something DeepSeek will excel at. I'm using GPT (Terra, Sol, Luna), Claude (Opus 5, Fable), Qwen 3.8 and GLM 5.3 Flash daily and have to vary which model I use where because there's a huge intelligence step function difference here. That's why this article is so useless:
Imagine someone trying to make the case that riding bicycles is a terrible experience and their whole argument is that they took a random cheapo bike with flat tires and rode it for 3min and that wasn't fun. Sure, but if you buy a 25k carbon bike you will have a different experience. I'd not trust that person. If someone told me they have 10 bikes they ride daily and can explain the differences, in detail, between their bikes, and what they excel at. I'd trust that person's opinion.
Excellent analogy. This paragraph invalidates the entire post and honestly just looks lazy. The author may be right anyway, but with that level of experience with these tools, he is really just guessing.
There’s a night-and-day difference between frontier and budget models, no question. But the issue isn't the tooling at all : if you put someone who doesn't know the rules of the road on a $15k carbon road bike, they're just gonna slam into a telephone pole at 30 mph instead of 6 mph
I got here and... I agreed with everything up to this point.
The author makes a good point. If you don't know what you're doing, AI accelerates that. No question.
But they put a whopping... ten bucks into using DeepSeek and weren't impressed with the initial results.
I know they try to cover this with "you just aren't prompting correctly!" but if, in 2026, you aren't able to have an LLM generate decent quality code... IDK what to tell you. Good luck I guess?
IMO given the position DeepSeek actually occupies in terms of its comparison to models only recently behind the frontier, if the counterargument is, at this point, still, "oh you need to use the frontier model and spend a bit more", or "you need to use multiple agents and loops and X and Y and Z", then it's not a very strong argument.
Given how good DeepSeek is, where it sits relative to frontier models people were raving about at launch and still favour over the latest, $10 into DeepSeek should have blown them away.
I am not using any frontier models (at the moment), and I understand their point to likely be correct.
This is an intuition that I feel comfortable reaching based on experiments precisely with open weights models that are behind the curve, roughly where the frontier models would have been a year or so back when all the businesses that should have appeared would have started, back when AI hype merchants were first touting its unique power to 10x coders to create new startups and industries that simply haven't materialised.
The model I favour at the moment, Muse Glimmer, is worse on every benchmark than every other model everyone loves. When it was announced, HN was full of the voices saying it was simply dead on arrival because Qwen 3.8 would blah blah blah.
And yet it's the one that is fully usable as a coding assistant because it appears properly calibrated for the task. It might even get me to noticeable productivity without adding significant new bugs.
My instinct is that this experience scales.
There is still so much "oh it'll be better around the next corner" or "skill issue" or "you just need to put this in your <whatever> file", so much "you just have to accept there will be more bugs", there is still so much cosmic ordering and cargo culting going on, and yet the complaints about the models being badly behaved, argumentative and writing badly also still continue.
I think most of us should be tired of the "in six to twelve months" of it all, but OpenAI and Anthropic still roll it out. It's long past time that progress is not just round the next corner.
The workflow isnt a single prompt then you have working code from the LLM, its 30 prompts of discussion and then reading each proposed edit and having tangent discussions until you're somewhat ok with it. Then reviewing the changeset as a whole and refactoring a few times
> but if, in 2026, you aren't able to have an LLM generate decent quality code... IDK what to tell you.
I think the author's point still stands. At some point you need to determine whether the output is of decent quality. A lot of people don't have the ability and experience to do that.
'Or else admit this is a dopamine game that makes you feel like The Universe's Most Special Programmer™ when it's really just gamified mass-scale intellectual dependency.'
> The chatbot recommended some of the dumbest shit you could possibly do
The "I tried it and it sucked" is borderline conspiratorial at this point. There are enough talented, thoughtful developers saying there's something real here, and it is worth believing them and investing some time to understand it, even if you come out the other side and decide you don't want to use LLMs.
Use a very good model. Set up a good harness. Spend some time on your system prompts and skills. Develop your intuitions about how the model works, what it's good at, how to scope the work, and how to steer it. Recognize when it's alleviating menial work and recognize when it's making choices you really need to understand yourself. Be patient and accept that the failures are going to be very painful for a while.
Don't write it off until you've genuinely seen the upsides.
If one truly believes it's not in a bubble, then borrow and leverage an unlimited amount and bet the house & your family on creating a business with infinite growth and value. Don't know how to do your own prompts? Borrow and then hire others to do it for you. If those who you hire don't know, they can hire others too.*
[* This is not financial advice. Please don't actually do this.]
His AI experience is $10 of DeepSeek - but I think he is right anyway in the main points.
You can most definitely ship crap much faster than you used to. It's obvious to anyone when you're shipping crap. And it seems everywhere I look people are shipping crap. Both software and writing.
The hardest part of shipping quality software is not and has never been "writing the code". The hard parts are product taste, architecture and ensuring your product actually solves the problems it should in an efficient and secure manner.
If you don't have an intuition of those, you will likely be shipping crap.
Yep, using AI to remove your thinking process and judgement, and just let it generate stuff as fast as possible, will definitely make you ship crap faster! If you did the same thing with a junior developer you would get the same result.
Where AI shines for me is accelerating the learning and exploration process. I can get up to speed with new tech fast. It is good at spotting issues in designs and code. It can knock out quick tests or benchmarks to support me. The quality of what I can produce with AI support is much higher than I could without it.
So it really just depends on what you use it for. If the goal is "replace humans and ship fast" that's one thing. If the goal is "explore the problem space in greater depth", it's another.
I think it depends on what area of the world you work. We recenty had one of our plant managers build a web portal to keep track of some of our operational tech, with features you wouldn't find in standard products. I've been turning it into a container app that can actually deploy safely into our cloud infrastrcture. It's quite frankly better quality than what most external software companies have provided us with in the past.
Don't get me wrong. It's not great. It would never pass any of our policies for things that actually operate stuff on the power grid, but as an administrative tool that can live in total isolation from the vital networks. It's perfectly fine. It's also not like we would have hired the best software companies to build it otherwise. We'd hire some low-level cheap consultant house who would then likely get cheap student labour to build it. With that in mind though, the AI is much better than what the realistic alternative would be.
Money wise it's also cheaper. It's been roughly €1000 + the time it's taken us both. If I had known they were doing it, I would have rolled out the developer cowork app/skills/whateveryoucallconfigurationsthesedays to them. This would have avoided their AI building it to be depoyed on a VM rather than in our managed k8s in our Azure. It would also have written the code a little different, used UV and maybe django rather than flask. But hey. For what it is, it's like a 90% cost saving compared to buying what would've been a less maintainable and lower quality system.
I think perhaps the greater issue will be finding people who want to extract the gold from the heap of shit and getting it to run in production. I don't personally mind, but it's not like any of my colleagues would've wanted the task.
With the models we got at out disposal, you can easily deploy a kickass internal tool, it's fine even for mvp's unless you're not handling sensitive user data, shooting yourself in the foot is easier than ever, and talking about code smell for most application (especially frontend) does it even matter?
I doubt it would be better served as a customized off the shelf software. They certainly could have done it with our existing systems with SmartSheet, Microsoft Fabric and/or Power Apps with SharePoint for document storage.
From an enterprise perspective this becomes complicated for various reasons. RBAC is one area. In the perfect world you have a system to handle roles and rights to every system, something that you can give managers access to so they can maintain the access available to their employees, something linked with HR. In reality you have EntraID with a hieracy which is sort of automated by HR data, but not really, because sometimes HR puts everyone on the CEO level by mistake, and, if you trusted HR as authoritative that would've just broken all the EU laws. So you have all those Entra groups and you need IT Operations to maintain them and since you want to build it on job roles and not people you'll typically not be able to maintain them in the off the shelf system. Which means that you would have had to build a web portal for the plant managers manager where they could maintain a couple of Entra groups in a web interface. That or you'll have to setup an IT support flow where you add yet another system that IT has to maintain access for.
Then we get to the actual customization. Maybe you buy a custom API on top of your BC365 platform. Maybe your C-levels deciced that paying €50k a year to avoid outages on major updates isn't worth the cost. Then when things predictably and completely avoidable fail you're going to hav to deal with the literal shitstorm. You'd think that all the people being locked out of their jobs and the €150k cost of getting an immediate and prioritised update to the system would mean you'd start paying for that $50k service after this. You'd be wrong. Ok, to be fair, in this particular example it would be a different scenario. For a small system like this you'd find a cheap consultant house in your area and get them to build the customization for you. Only they would outsource it to some solo developer who will build it in a way that basically requires that specific person to alter it. Then when it breaks or needs to be customized futher a year down the line, that person is no longer a solo developer. So you reach out to another cheap consultant house and do it all over again, from scratch.
This doesn't even mention how poorly all those 300 off the shelf systems work together. I mean, I don't maintain a SDK delivering a way to use Apache-Arrow to write and read parquet files from our datalake in the same manner for fun. I do it because those 600 container apps which basically simply translate data from one system to another need it to be as slim as possible.
for an administrative tool the criticality is not determined by the network boundary but by which decisions within the plant are based on these figures
it hasn't even been a year since Opus 4.5 and my work codebase is thoroughly ensloppified. From the top, the assumption is you don't edit it by hand. It was fine at first but hand editing is becoming more and more painful. LLM coding is a cancer in this way.
I tried really hard to get senior management to understand this where I work. Yes, AI is an accelerator, but that doesn't necessarily mean it's going to accelerate you in the "right" direction.
AI is like a tool used by mega-corporations to change the world according to what they want. We see this with the increase in RAM prices. I don't want to pay the overprice here - AI companies owe us money. People seem still mostly in the AI hype phase, but a lot more criticism has amplified in the last months. It is only a matter of time until the hype phase is over.
Why does it have to lead to a 10x increase in revolutionary companies?
Next to maintaining and expanding my own network of websites I 10xd writing boring CRUD applications for companies that were otherwise unable to afford it, making all their employees more productive. There's true economic value in that.
I have a coffee cup with the writing "Do stupid thing faster with cafe". That's how I feel about myself when I use AI carelessly... The speed with which I can make a mess is astronomical!
In general, the frontier models are not capable of reliably authoring non-trivial code without careful oversight yet. They are great at producing code that can pass tests, but not neccessarily a code review. This means if you care about code quality you still need a human in a loop understanding what has been done, and that becomes the bottleneck. And less disciplined folks will indeed become increasingly dependent.
However, over time the complexity of problems where you can get away with less/no oversight is increasing. And the models are already great at solving certain classes of problems where one doesn't really care that much about code quality, that wouldn't have even been attempted in a pre-LLM world. Over the weekend I was using Claude to add features to the compiled (no source available) firmware of one of my audio devices, adding workflow features by patching assembly and custom DSP code.
In coding, as with other areas, what's emerging is jagged intelligence.
I now started to us AI to help review my juniors PRs, because I couldn't keep up with the amount of code they ship. It started poorly, but now I have my method: I first read the code and flag the lines I'm not sure about, then ask any frontier model (I like Claude here for analysis, even if I don't use it for the rest) to explain the PR and to put effort on the parts I flagged (basically explain in detail the code, not only the PR), and to search through the libraries. Sometimes it notices something I would have missed (like missing an 'order_by' or off by one errors, because the underlying lib wasn't coded like the original AI pretended it was).
I also changed the way I do review because it has been more than a year and the juniors/new hire are still lost, wether on domain knowledge for the older new hire, or just capabilities for the juniors, and discussing with other departments, it's the same for like 95% of them. Now, rather than correcting the PR or adding a request for change, I add a whole unit/functional test to the PR and let that as an exercise to pass the test. They can use AI but I tell them to try to find what part of the code doesn't work before generating the fix, hopefully they'll take ownership of the code if I keep doing that.
even before AI very few developers cared about the actual craft. I'd say only about 3%. For a lot of people it's just a job, no matter if it's a corperate dev or a startup dev.
It's a different mindset. Has nothing to do with curiosity.
For me and you, "fixing" something means finding which assumption was violated and redesigning the solution in light of that. Sometimes this makes the solution smaller.
For a lot of people, "fixing" means "adding code to make it work". This always makes the solution larger.
The latter group can still be curious! But they're curious about which addition makes it work, not about their incorrect beliefs.
Usually something more subtle like adding an early return somewhere before the offending line, or inheriting from that class and blanking that method, or something along those lines.
What I saw in these cases, the result was a few levels removed from the root cause and they'd add the same workaround to like 5 different places (and miss one or two) instead of tracking it back to the origin where the fix was that simple.
One of my customers now added a policy that junior developers may use AI assistance, but are expected to write code by hand. Only proven developers can use AI to generate code, and are then expected to understand the generated code, and take ownership.
"because the underlying lib wasn't coded like the original AI pretended it was"
So, the models ARE dumb. They just are very good at finding patterns in their training data. I mean, when they code minecraft clones, it is not because they can cook up how to write minecraft clones, rather, their training data includes a lot of minecraft-like games code, and they just reuse that.
I don't disagree with the title of this weblog post, but its premise is wrong: the existence of AI has an impact on the total number of tech companies, but not on the number of companies that make it real big (the AirBnBs, Stripes and Dropboxes the author is looking for).
Bit of a humbling/jarring moment when I realized that people are doing real paid work using LLMs that they could not otherwise do. I mean, it's quite obvious I suppose. But up until now I just assumed it was only a (massive) catalyst for things people would already be able to do with enough time. But nope -- it seems people are right now employed in roles that they would not be able to fulfil the tasks within if AI wasn't there telling them what to write/say/produce. Nobody is really going to come out and say that ... it's not something the less-AI-literate superiors would take kindly to.
> people are doing real paid work using LLMs that they could not otherwise do
FWIW, this is not exactly new; those same people were just using other sources like Stack Overflow, blog posts, etc. before, cobbling together random code snippets, libraries, and so on without actually understanding any of that at a relevant detail level.
Sure, with LLMs, one can naturally tailor this much closer to the current need (or at least the need one thinks they have) and iterate ("spew") faster, but it's not a new phenomenon in general.
Yeh but I also mean outside of software engineering. Within the gamut of 'being a programmer' it seems fair to bleed into adjacent areas without too much cheek. We've all done it; it's part of the learning curve. But I was talking more about people in other knowledge work who have to come up with a lot of prose-like material about {insert thing}. Marketing, consultants, PMs, or even domain-specific analysts, .. ya know, the types of office jobs where people basically write emails, attend meetings, discuss reports, and produce mostly text-or-data artefacts all day long.
I think this is fine as long as the success criteria are strict enough that they are forced to learn something in the end.
Any significant testing will inevitably create that situation. The LLM won't have enough context to handle the more precise business requirements. The dev will have to read the code carefully and make their changes by hand. Additional rounds of testing may cause thrashing between regressed states until something clicks for the developer. That lightbulb going off is called "learning" and they are human after all!
Promotion into management isn't so different. They still experience similar thrashing and have to learn or be fired.
My point was not only that so much human learning happens out-of-band, but that it has to be fundamentally different from how an LLM builds context. I've never seen an LLM overcome the thrashing on its own. There's never any "lightbulb moment".
aka there are more bullshit artists around these days
i know of several engineers who produce absolute slop and who probably would have produced nothing at all in pre AI times and quickly fired. they act as an enormous drag on productivity.
I mentioned this then other day, my university class had maybe 80% of people who could not code. No joke. And a lot have gone onto to code professionally.
You can learn to code in a few months in your free time and get good in your job. That's different from refusing to learn it, because you outsource it.
Also coding is not that hard. You kinda only write the algorithms down you make up in your head. And you do have an algorithmic understanding when you graduate. You also invented some/several languages in during your studies. The learning an actual developed language IS learning, but it's just boring learning, nothing compared to what you did for your exams.
I’ve long been curious what you (and a lot of people that express this) feel the cutoff is to know how to code? Because obviously there’s a sliding scale between truly knowing very little, being able to adapt similar work, extensive modifications, coding everything from scratch, etc. on top of understanding certain programming concepts and organization.
Can you build a desktop/terminal app, web app/site, mobile app, or even a library without engaging AI is probably where I’d draw the line. Not everything you touch when you know how to code is doing the whole thing, but I’d expect you’d be able to piece it all together with some time and docs. Pick your own platform that suits too, I’m not saying iOS or Windows only.
For context to my above comment re university course.
The final project we had to do was make a very rudimentary room reservation server/client on the terminal. Java, however you wanted to store the data was fine, TCP sockets were the parameters given.
It took me all of a week to finish it. Lots struggled to even begin. We were given 10 weeks.
I didn’t know much then but I read the docs and figured it out.
> Can you build a desktop/terminal app, web app/site, mobile app, or even a library without engaging AI
If companies would start hiring people who are fully experienced and qualified but can't do the monkey dance of performing live coding, maybe they would find these types of programmers again...
At a FAANG adjacent company in 2017 we hired a guy from Google with 4 years experience who passed our very hard coding interviews. He literally couldnt code. It blew my mind.
My families business is bookkeeping, they hired someone who graduated with an accounting degree. They didn’t grasp the basics of bookkeeping nor accounting once employed.
You’d think Google who my understanding pride themselves on technical chops would weed this out with all the interviews they do. But maybe it’s just big places have more cracks for people slip into.
That's one of the few consumer-facing areas where there are still standards in place, namely PCI-DSS. As far as I know the audits require the name of a human who is responsible for payment security. Card companies can one-hit kill your startup if you're breaking those rules (maybe purely blockchain startups are exempt).
Yes, you can offload this to stripe, but then your app should never see the card number and certainly not the CVV. You end up storing these, even by accident, both stripe and the card companies will hate you.
> and ruining the quality of internet search engines.
Tbf, I'm not sure we can really lay this one at the feet of GenAI. The SEO bros had pretty thoroughly ruined search before LLMs took off - the process just accelerated a little at that point.
> “after four years of open source LLMs, we should have three AirBnBs, two Stripes, and three Dropboxes thanks to the power of AI.”
Airbnb, Stripe and Dropbox were created in a different time when the market was much less competitive.
Saturation of software development velocity doesn’t increase large scale product opportunities in the market. It can also mean that opportunities get filled even more quickly by niche players, and nobody gets to grow to Airbnb scale.
IMO the latter is what’s currently happening. AI-powered companies are like little mammals scurrying around between the feet of the dinosaurs, and commentators like the OP look at the evolution of the brontosaurus as evidence that the mammals don’t seem to be growing as they should.
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[ 0.22 ms ] story [ 40.8 ms ] threadNow with AI I can 10x my output and 10x my bad code!
Why not use actual frontier models, and you know do some real research, before writing a blog post?
It is funny that only real research on productivity gains from AI shows at best very minimal gains, but AI bros will always tell you "no no no, you have used wrong model, try a different one, there are more of them, you have to try, trust me" and call that a "research".
Looking forward to the authors next blog post on how after driving one car they find that all cars are slow and uncomfortable. Then the follow up after trying one phone and that all phones have bad cameras and battery life.
My point is, there is a monumental difference between flying Ryanair and Emirates First Class for example.
Seems like they've already done more research than you. Their results not matching your expectations does not indicate a lack of research. If you feel they missed something, why don't you go spend your own money on whatever frontier model you want and go publish a post with your own conclusions.
It's like playing one video game and then claiming all video games are boring and have bad gameplay and stories.
* stretching the timeline: the actual real programming ability appeared in LLMs in the last 6-8 months, not 3-4 years,
* using the weakest possible tool: and I bought $10 worth of DeepSeek credits that is a far cry from Claude with Fable.
Also, I know nothing about marathons but for most uses putting the app, database, and background processes on the same server is very much the right starting point. With the next steps being employing Cloudflare or similar solutions long before managing a fleet of servers.
And since then there's been 3 more "now real programming ability has been made available and previous stuff was just toy examples" cycles (summer 2025, winter 2025 and spring 2026)
Looking forward to the next "everything before this was trivial and bad, here's the good stuff" moment
Or B that the ideas are there but are not get released as the code is ai slop?
This is fair to say if someones last experience with AI was copy-pasting code into GPT3 chat windows years ago, but Deepseek is a more than capabale model and enough for someone to get an informed opinion about the technology.
If people have actual counter argument, use those. And if some of those counter argument are "What you say isn't possible, the neweste model can do and here are examples of that", that is fine.
But a blanket "Nuh-uh, it wasn't Model X" is not only a poor argument but also automatically invalidates any criticism when a new, better model comes out - and that can't be the basis of a good argument.
The field is moving fast, and asking for scientific arguments is not realistic. It takes an extreme amount of effort to show what exactly is different.
We were in a similar position with static vs dynamic typing for decades. There is still no scientific proof that one is better than the other, but it is quite obvious to professionals which flavor works better in a given situation.
So, even though the argument might be sloppy, I subscribe to it. Using DeepSeek to dismiss better models is the bad argument here.
Edit: added "(for some)" as a disclaimer that you still need to be a fairly decent programmer to actually benefit.
That still is very much not the case. Every model, yes, including $most_recent_model, needs supervision or you will get burned.
You don't have to pay money to use Codex, there is a very generous free tier that costs you nothing, you just have to accept being told you're out of tokens every day. Because your token limits are low, you need to make sure that you accept or reject everything manually and when it tells you that it wants to run a command you have to paste in the command into your terminal and only paste the relevant output back otherwise it floods the context window.
No, they still aren't good.
What the author of the article is doing is dismissing a technology so disruptive that it's basically all everyone's talking about in the "tech space" at the moment (I mean look at HN frontpage for the past few months), by trying a relatively mediocre (but still quite good) model for about 10 seconds.
The reality is that frontier models suddenly got very good in the past 3-6 months. It has it's problems, and you need to learn how to use this new tool (as with any tool).
But models can and do generate good code. They also can and do generate absolute garbage (even Fable).
You need a good harness, tools to help the model check it's own output, good context, and a good idea of what you actually want. If you have those 4 things, the chances of generating absolute garbage are pretty slim (but yeah, still there).
Wow. I guess that’s the punchline!
Actually, I think the author put DeepSeek on purpose to avoid the obvious ChatGPT/Claude comparison — because whatever he chose, there would be a question of why model A and not B, while the point of the article isn't about models comparison at all.
Imagine someone trying to make the case that riding bicycles is a terrible experience and their whole argument is that they took a random cheapo bike with flat tires and rode it for 3min and that wasn't fun. Sure, but if you buy a 25k carbon bike you will have a different experience. I'd not trust that person. If someone told me they have 10 bikes they ride daily and can explain the differences, in detail, between their bikes, and what they excel at. I'd trust that person's opinion.
The author makes a good point. If you don't know what you're doing, AI accelerates that. No question.
But they put a whopping... ten bucks into using DeepSeek and weren't impressed with the initial results.
I know they try to cover this with "you just aren't prompting correctly!" but if, in 2026, you aren't able to have an LLM generate decent quality code... IDK what to tell you. Good luck I guess?
Given how good DeepSeek is, where it sits relative to frontier models people were raving about at launch and still favour over the latest, $10 into DeepSeek should have blown them away.
I am not using any frontier models (at the moment), and I understand their point to likely be correct.
This is an intuition that I feel comfortable reaching based on experiments precisely with open weights models that are behind the curve, roughly where the frontier models would have been a year or so back when all the businesses that should have appeared would have started, back when AI hype merchants were first touting its unique power to 10x coders to create new startups and industries that simply haven't materialised.
The model I favour at the moment, Muse Glimmer, is worse on every benchmark than every other model everyone loves. When it was announced, HN was full of the voices saying it was simply dead on arrival because Qwen 3.8 would blah blah blah.
And yet it's the one that is fully usable as a coding assistant because it appears properly calibrated for the task. It might even get me to noticeable productivity without adding significant new bugs.
My instinct is that this experience scales.
There is still so much "oh it'll be better around the next corner" or "skill issue" or "you just need to put this in your <whatever> file", so much "you just have to accept there will be more bugs", there is still so much cosmic ordering and cargo culting going on, and yet the complaints about the models being badly behaved, argumentative and writing badly also still continue.
I think most of us should be tired of the "in six to twelve months" of it all, but OpenAI and Anthropic still roll it out. It's long past time that progress is not just round the next corner.
Nobody is. The only difference is that some of us are willing to say the emperor has no clothes.
I think the author's point still stands. At some point you need to determine whether the output is of decent quality. A lot of people don't have the ability and experience to do that.
Disesdi Shoshana Cox
Seriously, that was the lowest level of skill I saw in 30 years of development.
Now we have people who can't even do that proudly showing off PRs to widely used products.
"B-b-but I do the systems design and hard thinking".
Sure, buddy.
The "I tried it and it sucked" is borderline conspiratorial at this point. There are enough talented, thoughtful developers saying there's something real here, and it is worth believing them and investing some time to understand it, even if you come out the other side and decide you don't want to use LLMs.
Use a very good model. Set up a good harness. Spend some time on your system prompts and skills. Develop your intuitions about how the model works, what it's good at, how to scope the work, and how to steer it. Recognize when it's alleviating menial work and recognize when it's making choices you really need to understand yourself. Be patient and accept that the failures are going to be very painful for a while.
Don't write it off until you've genuinely seen the upsides.
[* This is not financial advice. Please don't actually do this.]
you want more SaaS?
You can most definitely ship crap much faster than you used to. It's obvious to anyone when you're shipping crap. And it seems everywhere I look people are shipping crap. Both software and writing.
The hardest part of shipping quality software is not and has never been "writing the code". The hard parts are product taste, architecture and ensuring your product actually solves the problems it should in an efficient and secure manner.
If you don't have an intuition of those, you will likely be shipping crap.
Where AI shines for me is accelerating the learning and exploration process. I can get up to speed with new tech fast. It is good at spotting issues in designs and code. It can knock out quick tests or benchmarks to support me. The quality of what I can produce with AI support is much higher than I could without it.
So it really just depends on what you use it for. If the goal is "replace humans and ship fast" that's one thing. If the goal is "explore the problem space in greater depth", it's another.
Don't get me wrong. It's not great. It would never pass any of our policies for things that actually operate stuff on the power grid, but as an administrative tool that can live in total isolation from the vital networks. It's perfectly fine. It's also not like we would have hired the best software companies to build it otherwise. We'd hire some low-level cheap consultant house who would then likely get cheap student labour to build it. With that in mind though, the AI is much better than what the realistic alternative would be.
Money wise it's also cheaper. It's been roughly €1000 + the time it's taken us both. If I had known they were doing it, I would have rolled out the developer cowork app/skills/whateveryoucallconfigurationsthesedays to them. This would have avoided their AI building it to be depoyed on a VM rather than in our managed k8s in our Azure. It would also have written the code a little different, used UV and maybe django rather than flask. But hey. For what it is, it's like a 90% cost saving compared to buying what would've been a less maintainable and lower quality system.
I think perhaps the greater issue will be finding people who want to extract the gold from the heap of shit and getting it to run in production. I don't personally mind, but it's not like any of my colleagues would've wanted the task.
There's not a lot of software where users dont really care if it goes wrong.
From an enterprise perspective this becomes complicated for various reasons. RBAC is one area. In the perfect world you have a system to handle roles and rights to every system, something that you can give managers access to so they can maintain the access available to their employees, something linked with HR. In reality you have EntraID with a hieracy which is sort of automated by HR data, but not really, because sometimes HR puts everyone on the CEO level by mistake, and, if you trusted HR as authoritative that would've just broken all the EU laws. So you have all those Entra groups and you need IT Operations to maintain them and since you want to build it on job roles and not people you'll typically not be able to maintain them in the off the shelf system. Which means that you would have had to build a web portal for the plant managers manager where they could maintain a couple of Entra groups in a web interface. That or you'll have to setup an IT support flow where you add yet another system that IT has to maintain access for.
Then we get to the actual customization. Maybe you buy a custom API on top of your BC365 platform. Maybe your C-levels deciced that paying €50k a year to avoid outages on major updates isn't worth the cost. Then when things predictably and completely avoidable fail you're going to hav to deal with the literal shitstorm. You'd think that all the people being locked out of their jobs and the €150k cost of getting an immediate and prioritised update to the system would mean you'd start paying for that $50k service after this. You'd be wrong. Ok, to be fair, in this particular example it would be a different scenario. For a small system like this you'd find a cheap consultant house in your area and get them to build the customization for you. Only they would outsource it to some solo developer who will build it in a way that basically requires that specific person to alter it. Then when it breaks or needs to be customized futher a year down the line, that person is no longer a solo developer. So you reach out to another cheap consultant house and do it all over again, from scratch.
This doesn't even mention how poorly all those 300 off the shelf systems work together. I mean, I don't maintain a SDK delivering a way to use Apache-Arrow to write and read parquet files from our datalake in the same manner for fun. I do it because those 600 container apps which basically simply translate data from one system to another need it to be as slim as possible.
Am I jaded? Sure. But who isn't in enterprise IT?
Next to maintaining and expanding my own network of websites I 10xd writing boring CRUD applications for companies that were otherwise unable to afford it, making all their employees more productive. There's true economic value in that.
However, over time the complexity of problems where you can get away with less/no oversight is increasing. And the models are already great at solving certain classes of problems where one doesn't really care that much about code quality, that wouldn't have even been attempted in a pre-LLM world. Over the weekend I was using Claude to add features to the compiled (no source available) firmware of one of my audio devices, adding workflow features by patching assembly and custom DSP code.
In coding, as with other areas, what's emerging is jagged intelligence.
I also changed the way I do review because it has been more than a year and the juniors/new hire are still lost, wether on domain knowledge for the older new hire, or just capabilities for the juniors, and discussing with other departments, it's the same for like 95% of them. Now, rather than correcting the PR or adding a request for change, I add a whole unit/functional test to the PR and let that as an exercise to pass the test. They can use AI but I tell them to try to find what part of the code doesn't work before generating the fix, hopefully they'll take ownership of the code if I keep doing that.
this makes me kinda sad. they don't do this on their own? are they not even a little curious
For me and you, "fixing" something means finding which assumption was violated and redesigning the solution in light of that. Sometimes this makes the solution smaller.
For a lot of people, "fixing" means "adding code to make it work". This always makes the solution larger.
The latter group can still be curious! But they're curious about which addition makes it work, not about their incorrect beliefs.
What if the fix requires deleting a single line of code?
Is the additive fix to enshroud it in if (0) { } ?
Some are. Some aren't. Usually a good indicator of career trajectory.
So, the models ARE dumb. They just are very good at finding patterns in their training data. I mean, when they code minecraft clones, it is not because they can cook up how to write minecraft clones, rather, their training data includes a lot of minecraft-like games code, and they just reuse that.
Sure, with LLMs, one can naturally tailor this much closer to the current need (or at least the need one thinks they have) and iterate ("spew") faster, but it's not a new phenomenon in general.
Any significant testing will inevitably create that situation. The LLM won't have enough context to handle the more precise business requirements. The dev will have to read the code carefully and make their changes by hand. Additional rounds of testing may cause thrashing between regressed states until something clicks for the developer. That lightbulb going off is called "learning" and they are human after all!
They are fired, get promoted to management, or learn the technical aspects.
My point was not only that so much human learning happens out-of-band, but that it has to be fundamentally different from how an LLM builds context. I've never seen an LLM overcome the thrashing on its own. There's never any "lightbulb moment".
If the company want's hire someone who doesn't have a clue and only uses SO, that's of course fine, but I doubt, that this is the case.
i know of several engineers who produce absolute slop and who probably would have produced nothing at all in pre AI times and quickly fired. they act as an enormous drag on productivity.
AI is a godsend to this cohort of code monkies.
Edit: we graduated in 2006.
Also coding is not that hard. You kinda only write the algorithms down you make up in your head. And you do have an algorithmic understanding when you graduate. You also invented some/several languages in during your studies. The learning an actual developed language IS learning, but it's just boring learning, nothing compared to what you did for your exams.
For context to my above comment re university course. The final project we had to do was make a very rudimentary room reservation server/client on the terminal. Java, however you wanted to store the data was fine, TCP sockets were the parameters given.
It took me all of a week to finish it. Lots struggled to even begin. We were given 10 weeks.
I didn’t know much then but I read the docs and figured it out.
If companies would start hiring people who are fully experienced and qualified but can't do the monkey dance of performing live coding, maybe they would find these types of programmers again...
Wasn't the workflow there always to take the example code of whatever library or framework it was and then customize it?
I could definitely never start a react or android or c# or whatever application from scratch with no resources. Nor would I want to because why.
Works with C I guess though. Given that the contract is so small.
But that also breaks down the moment you want to write C for some uC, and then there's tons of boilerplate again you just pull from the sdk example.
My families business is bookkeeping, they hired someone who graduated with an accounting degree. They didn’t grasp the basics of bookkeeping nor accounting once employed.
You’d think Google who my understanding pride themselves on technical chops would weed this out with all the interviews they do. But maybe it’s just big places have more cracks for people slip into.
That's one of the few consumer-facing areas where there are still standards in place, namely PCI-DSS. As far as I know the audits require the name of a human who is responsible for payment security. Card companies can one-hit kill your startup if you're breaking those rules (maybe purely blockchain startups are exempt).
Yes, you can offload this to stripe, but then your app should never see the card number and certainly not the CVV. You end up storing these, even by accident, both stripe and the card companies will hate you.
Tbf, I'm not sure we can really lay this one at the feet of GenAI. The SEO bros had pretty thoroughly ruined search before LLMs took off - the process just accelerated a little at that point.
Airbnb, Stripe and Dropbox were created in a different time when the market was much less competitive.
Saturation of software development velocity doesn’t increase large scale product opportunities in the market. It can also mean that opportunities get filled even more quickly by niche players, and nobody gets to grow to Airbnb scale.
IMO the latter is what’s currently happening. AI-powered companies are like little mammals scurrying around between the feet of the dinosaurs, and commentators like the OP look at the evolution of the brontosaurus as evidence that the mammals don’t seem to be growing as they should.
You won’t get capital let alone VC if you’re not AI.
It’s infected everything much like crypto did just 3-4 years ago.
Neither was ever about some bottleneck on pumping out code and everything about marketing and network effects