I spend hours on a spec, working with Claude Code to first generate and iterate on all the requirements, going over the requirements using self-reviews in Claude first using Opus 4.5 and then CoPilot using GPT-5.2. The self-reviews are prompts to review the spec using all the roles and perspectives it thinks are appropriate. This self review process is critical and really polishes the requirements (I normally run 7-8 rounds of self-review).
Once the requirements are polished and any questions answered by stakeholders I use Claude Code again to create a extremely detailed and phased implementation plan with full code, again all in the spec (using a new file is the requirements doc is so large is fills the context window). The implementation plan then goes though the same multi-round self review using two models to polish (again, 7 or 8 rounds), finalized with a review by me.
The result? I can then tell Claude Code to implement the plan and it is usually done in 20 minutes. I've delivered major features using this process with zero changes in acceptance testing.
What is funny is that everything old is new again. When I started in industry I worked in defense contracting, working on the project to build the "black box" for the F-22. When I joined the team they were already a year into the spec writing process with zero code produced and they had (iirc) another year on the schedule for the spec. At my third job I found a literal shelf containing multiple binders that laid out the spec for a mainframe hosted publishing application written in the 1970s.
Looking back I've come to realize the agile movement, which was a backlash against this kind of heavy waterfall process I experienced at the start of my career, was basically an attempt to "vibe code" the overall system design. At least for me AI assisted mini-waterfall ("augmented cascade"?) seems a path back to producing better quality software that doesn't suffer from the agile "oh, I didn't think of that".
I'm not saying I don't believe your report - maybe you are working in a domain where everything is super deterministic. Anyway, I don't.
Writing a spec is akin to "working backwards" (or future backwards thinking, if you like) -- this is the outcome I want, how do I get there?
The process of writing the spec actually exposes the edge cases I didn't think of. It's very much in the same vein as "writing as a tool of thought". Just getting your thoughts and ideas onto a text file can be a powerful thing. Opus 4.5 is amazing at pointing out the blind spots and inconsistencies in a spec. The spec generator that I use also does some reasoning checks and adds property-based test generation (Python Hypothesis -- similar to Haskell's Quickcheck), which anchors the generated code to reality.
Also, I took to heart Grant Slatton's "Write everything twice" [1] heuristic -- write your code once, solve the problem, then stash it in a branch and write the code all over again.
> Slatton: A piece of advice I've given junior engineers is to write everything twice. Solve the problem. Stash your code onto a branch. Then write all the code again. I discovered this method by accident after the laptop containing a few days of work died. Rewriting the solution only took 25% the time as the initial implementation, and the result was much better. So you get maybe 2x higher quality code for 1.25x the time — this trade is usually a good one to make on projects you'll have to maintain for a long time.
This is effective because initial mental models of a new problem are usually wrong.
With a spec, I can get a version 1 out quickly and (mostly) correctly, poke around, and then see what I'm missing. Need a new feature? I tell the Opus to first update the spec then code it.
And here's the thing -- if you don't like version 1 of your code, throw it away but keep the spec (those are your learnings and insights). Then generate a version 2 free of any sunk-cost bias, which, as humans, we're terrible at resisting.
Spec-driven development lets you "write everything twice" (throwaway prototypes) faster, which improves the quality of your insights into the actual problem. I find this technique lets me 2x the quality of my code, through sheer mental model updating.
And this applies not just to coding, but most knowledge work, including certain kinds of scientific research (s/code/LaTeX/).
Having part of my background in Functional Safety, I have seen it done many times, and it can most definitely be done.
It is just it can't be done in the sort of time frames that people who do not specify before coding, are used to.
But, if you can't afford to move fast and break tings, because it is an airplane, or train signaling system, or complicated elevator system with multiple cars in the same shaft, then you generally write no code until you know exactly what you want the code to do (and more importantly, not do).
They survive by being modified and I don't think that invalidates the process that got them in front of people faster than would otherwise have been possible.
This isn't a defence of waterfall though. It's really about increasing the pace of agile and the size of the loop that is possible.
Agile was really pushing to make sure companies could get software live before they died (number 1) and to remedy the anti-pattern that appeared with number 2 where non-technical business people would write the (half-assed) spec and then technical people would be expected do the monkey work of implementing it.
Agile core is the feedback loop. I can't believe people still don't get it. Feedback from reality is always faster than guessing on the air.
Waterfall is never great. The only time when you need something else than Agile is when lives are at stake, you need there formal specifications and rigorous testing.
SDD allows better output than traditional programming. It is similar to waterfall in the sense that the model helps you to write design docs in hours instead of days and take more into account as a result. But the feedback loop is there and it is still the key part in the process.
Mostly I’ve seen agile as, let’s do the same thing 3x we could have done once if we spent time on specs. The key phrase here is “requirements analysis” and if you’re not good at it either your software sucks or you’re going to iterate needlessly and waste massive time including on bad architecture. You don’t iterate the foundation of a house.
I see scenarios where Agile makes sense (scoped, in house software, skunk works) but just like cloud, jwts, and several other things making it default is often a huge waste of $ for problems you/most don’t have.
Talk to the stakeholders. Write the specs. Analyze. Then build. “Waterfall” became like a dirty word. Just because megacorps flubbed it doesn’t mean you switch to flying blind.
With the rise of AI, maybe programmers will be put back in their rightful place, as contributors of the final small piece of the development process: a translation from business terms to the language of the computer. Programming as a profession should, by all rights, be obsolete. We should be able to express the solution directly in business terms and have the translation take place automatically. Maybe that day will be here soon.
The key was this: "the requirements are polished and any questions answered by stakeholders"
We simply knew precisely what we were meant to be creating before we started creating it. I wonder to what degree the magic of "spec driven development" as you call it is just that, and using Claude code or some other similar is actually just the expression of being forced to understand and express clearly just what you actually want to create (compared to the much more prevalent model of just making things in the general direction and seeing how it goes).
If you already know the requirements, it doesn't need to come into play.
If you already know the requirements, and they aren't going to change for the duration of the project, then you don't need agile.
And if you have the time. I recently was on a project with a compressed timeline. The general requirements were known, but not in perfect detail. We began implementation anyway, because the schedule did not permit a fully phased waterfall. We had to adjust somewhat to things not being as we expected, but only a little - say, 10%. We got our last change of requirements 3 or 4 weeks before the completion of implementation. The key to making this work was regular, detailed, technical conversations between the customer's engineers, the requirements writers, and our implementers.
https://www.goodreads.com/book/show/15182720-design-by-contr...
but using a Large-Language-Model rather than a subordinate team?
c.f., https://se.inf.ethz.ch/~meyer/publications/old/dbc_chapter.p...
No matter how much I tell it that it is a "professional experienced 10x developer versed in modern C++, a second coming of Stroustrup" in per-project or global config files it still keeps spewing the same crap big (like manual memory management instead of RAII here and there, initializing fields in ctor body instead of initializer list, having manual init/cleanup methods in classes instead of a proper ctor/dtor design to ensure that objects are always in a consistent state, bunch of other anti-patterns, etc.) and small (checking for nullptr before passing the pointer to delete/free, manually instantiating objects as argument to shared_ptr ctor instead of make_shared, endlessly casting stuff around back and forth instead of designing data types properly, etc.).
Which makes sense I guess because it is how average C++ code on GitHub looks like unfortunately and that is what all those models were trained on, but I keep feeling like my job turning into performing endless code review for a not-very- bright junior developer that just refuses to learn...
On the other hand, LLMs are great at Go because Go was designed for average engineers at scale, and LLMs behave like fast average engineers. Go as a language was designed to support minimal cleverness (there's only so many ways to do things, and abstractions are constrained). This kind of uniformity is catnip for LLM training.
We know old-style classic waterfall lacks flexibility and agile lacks planning, but I don't see a reason why not to switch back and forth multiple times in the same project.
Which AI, least effort is the specs so that’s the “greatest thing to do” again.
using a new file IF the requirements doc is so large IT fills the context window
Is not a gift if it was stolen.
Anyway, in my opinion the code that was generated by the LLM is yours as long as you're responsible for it. When I look at a PR I'm reading the output of a person, independently of the tools that person used.
There's conflict perhaps when the submitter doesn't take full ownership of the code. So I agree with Antirez on that part
Yeah, I had a visceral reaction to that statement.
Code is the expression of knowledge and can be protected by copyright.
A lot of the popular licenses on GitHub (like MIT) permits you to use a piece of code on the condition that you credit the original author. If an LLM outputs code from such a project (or remixes code from several such projects) then it needs to credit the original authors or be in violation.
If Disney's intellectual property can be stolen and needs to be protected for 95+ years by copyright then surely the bedroom programmers' labor deserves the same protections.
I disagree. The code you wrote is a collaboration with the model you used. To frame it this way, you are taking credit for the work the model did on your behalf. There is a difference between I wrote this code entirely by myself and I wrote the code with a partner. For me, it is analogous to the author of the score of an opera taking credit for the libretto because they gave the libretto author the rough narrative arc. If you didn't do it yourself, it isn't yours.
I generally prefer integrated works or at least ones that clearly acknowledge the collaboration and give proper credit.
This seems like intentionally conflating them to imply that appropriating code for model training is a criminal offense, when, even in the most anti-AI, pro-IP view, it is plainly not.
And in practice, tools like NPM makes sure to output all of the libraries' licenses.
Agentic programming is at the end of the day a higher level auto complete, with extremely fuzzy matching on English.
But when you write a block and you let copilot complete 3, 4, 5 statements. Are you really writing the code?
The distinction drawn between both concepts matters. The expertise is in knowing what to spec and catching when the output deviates from your design. Though, the tech is so good now that a carefully reviewed spec will be reliably implemented by a state-of-the-art LLM. The same LLM that produces mediocre code for a vague request will produce solid code when guided by someone who understands the system deeply enough to constrain it. This is the difference between vibe coding and zen coding.
Zen coders are masters of their craft; vibe coders are amateurs having fun.
And to be clear, nothing wrong with being an amateur and having fun. I "vibe code" several areas with AI that are not really coding, but other fields where I don't have professional knowledge in. And it's great, because LLMs try to bring you closer to the top of human knowledge on any field, so as an amateur it is incredible to experience it.
If you're this meticulous is it really any faster than writing code manually? I have found that in cases where I do care about the line-by-line it's actually slower to run it through Claude. It's only where I want to shovel it out that it's faster.
Sometimes you only care about the high level aspect of it. The requirements and the high-level specification. But writing the implementation code can take hours if you're unfamiliar with a specific library, API or framework.
"review every diff line by line" is maybe not the best way to have described it, I essentially I meant that I review the AI's code as if it were a PR written by a team member, so I'd still care about alignment with the rest of the codebase, overall quality, reasonable performance, etc.
Please don’t, it’s just my day job.
This is beautiful to see, their mastery harnessing the power of the intelligent machine tools to design, understand and build.
This is like seeing a master of image & light like michelangelo receiving a camera, photoshop and a printer. It's an exponential elevation of the art.
But to become a master like michelangelo one had to dedicate herself to the craft of manually mixing and applying materials to bend and modulate light, slowly building and consolidating those neural pathways by reflection and, most of all, practice, until those skills became as natural as getting up or bringing a hand to the mouth. When that happened, art flowed from her mind to the physical world and the body became the vessel of intuition.
A master like antirez had to wrap his head around concepts alien to the human mind. Bits, bytes, arrays, memory layout, processors, compilers, interfaces, abstractions, constraints, types, concurrency do not exist in the savannas that forged brains. Had to comprehend and learn to use his own cognitive capabilities and restrictions to know at what level to break the code units and the abstraction boundaries. At the very top, master this in a level so high that software became like Redis: beautiful, powerful and so elevated in the art that it became simpler, not more complex. It's Picasso drawing a dog.
The intelligent software building machines can do things no human manually can (given the same time, humans die, get old or get bored), but they are not brush and canvas. They function in another way, the mind needs other paths to master them. The path to master them is not the same path to master artisanal software building.
So, this new generation, wanting to build things not possible to the artisan, will become masters of another craft, one we right now cannot even comprehend or imagine, in the same way michelangelo could never imagine the level of control over light the modern photography masters have.
Me, not a master, but having dedicated my whole life to artisanal software building, am excited to receive and use the new tools, to experiment the new craft. Also frightened by the uncertainty of this new world.
What a time to be alive.
I'm told that chess is more popular than ever, despite it being decades since a human could dream of beating a top computer at it.
What? He is mostly a AI influencer at this stage, even without getting paid for it (I think). There are always gonna be people writing code, people writing music, just because a machine can write code doesnt change the fact coding itself is a fun exercise.
>A master like antirez had to wrap his head around concepts alien to the human mind. Bits, bytes, arrays, memory layout, processors, compilers, interfaces, abstractions, constraints, types, concurrency do not exist in the savannas that forged brains.
You still need to know these things if you're doing anything more complicated than making some CRUD dashboard. LLMs assist with some code generation, and assist with some knowledge lookup. That's pretty much it.
What seems to be the case is that you need to know everything you needed to know before, and* become good at leveraging AI tooling to make you go faster.
*Even this is optional. There is absolutely nothing stopping anyone from just ignoring everything about AI and keep developing software like pre-2022. The efficiency difference isn't even significance in the grand scheme of things. It's not like people had reams of perfect software specs just lying around waiting to be implemented. That's just not how people develop software; usually the spec emerges while you're writing the program.
Those guys? Masters. Their craft? Reinvented, rearranged, some parts of it abstracted away by a more intelligent machine.
Current masters don’t know about memory drums and their read latency, nor about assembly instruction set execution time, but they are still crafting interaction between machines and the physical world.
So the next generation will be master of what? Which skills will they possess?
Unless you would rather be a calligrapher than a novelist.
People need to stop apologizing for their work product because of the tools they use. Just make the work product better and you don't have to apologize or waste people's time.
Especially given that you have these tools to make cleanup easier (in theory)!
"4GL" was a phase in the 1980s when very high level languages very provided by software companies, often integrating DB access and especially suited for particular domains. The idea was that one could focus more on the actual problem rather than having to write boilerplate needed to solving it.
LLMs permit to go from natural language specification to draft implementation. If one is lucky, it runs and produes the desired results right away; more often, one needs to revise the code base iteratively, again navigated by NL commands, to fix errors, to change the design based on reviewing the first shot at it, to add features etc.
I feel like this wording isn't great when there are many impactful open source programmers who have explicitly stated that they don't want their code used to train these models and licensed their work in a world where LLMs didn't exist. It wasn't their "gift", it was unwillingly taken from them.
> I'm a programmer, and I use automatic programming. The code I generate in this way is mine. My code, my output, my production. I, and you, can be proud.
I've seen LLMs generate code that I have immediately recognized as being copied a from a book or technical blog post I've read before (e.g. exact same semantics, very similar comment structure and variable names). Even if not legally required, crediting where you got ideas and code from is the least you can do. While LLMs just launder code as completely your own.
That’s been the fate of many creators since the dawn of time. Kafka explicitly stated that he wanted his works to be burned after his death. So when you’re reading about Gregor’s awkward interactions with his sister, you’re literally consuming the private thoughts of a stranger who stated plainly that he didn’t want them shared with anyone.
Yet people still talk about Kafka’s “contribution to literature” as if it were otherwise, with most never even bothering to ask themselves whether they should be reading that stuff at all.
But it's true much of his work was unpublished when he died and was "rescued" or "stolen", depending on what narrative you prefer.
LLMs are doing this on an industrial scale.
Yes but you can also ask the developer (wheter in libera.irc, or say if its a foss project on any foss talk, about which books and blogs they followed for code patterns & inspirations & just talk to them)
I do feel like some aspects of this are gonna get eaten away by the black box if we do spec-development imo.
There are subtle legal differences between "free open source" licensing and putting things in the public domain. If you use an open source license, you could forbid LLM training (in licensing law, contrary to all other areas of law, anything that is not granted to licensees is forbidden). Then you can take the big guys (MSFT, Meta, OpenAI, Google) to court if you can demonstrate they violated your terms.
If you place your software into the public domain, any use is fair, including ways to exploit the code or its derivatives not invented at the time of release.
Curiosly, doesn't the GPL even imply that if you pre-tain an LLM with GPLed code and use it to generate code (Claude Code etc.) that all generated code -- as derived intellectual property that it clearly is -- must also be open sourced as per GPL terms? (It would seem in the spirit of the licensors.) Haven't seen this raised or discussed anywhere yet.
Established OSS licenses are all from before anyone imagined that LLMs would come into existence, let alone train on and then generate code. Discrimination on purpose is counter to OSI principles (https://opensource.org/osd):
> 6. No Discrimination Against Fields of Endeavor
> The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research.
The GPL argument you describe hinges on making the legal case that LLMs produce "derived works". When the output can't be clearly traced to source input (even the system itself doesn't know how) it becomes rather difficult to argue that in court.
If the courts decide to apply the law as you assume the AI companies are all dead. But they are all betting that's not going to be the case. And since so much of the industry is taking the bet with them... The courts will take that into account
You can do whatever you want with a gift. Once you release your code as free software, it is no longer yours. Your opinions about what is done with it are irrelevant.
For example: MIT license states has this clause "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software."
It stands to reason that if an LLM outputs something based on MIT-licensed code then that output should at least contain that copyright because it's what the original author wished.
And I saw a comment below arguing that knowledge cannot be copyrighted, but the code is an expression of that knowledge and that most certainly can be protected by copyright.
However, if I was using a more recent/niche/unknown theorem, it would absolutely be considered bad practice not to cite where I got it from.
Yes. Exactly. As a developer in that case I feel almost violated in my trust in “the internet.” Well it’s even worse, I did not really trust it, but did not think it could be that bad.
I guess the difference is AI companies bad? This is transformative technology creating trillions in value and democratizing information, all subsidized by VC money. Why would anyone in open source who claims to have noble causes be against this? Because their repo will no longer get stars? Because no one will read their asinine stack overflow answer?
https://en.wikipedia.org/wiki/Google_LLC_v._Oracle_America,_....
Don't fall into the trap of thinking "if I don't heavily adopt Claude Code and agentic flows today I'll be working at Subway tomorrow." There's an unhealthy AI hype cottage industry right now and you aren't beholden to it. Change comes slowly, is unpredictable, and believe it or not writing Redis and linenoise.c doesn't make someone clairvoyant.
My advice would be to avoid feeling compelled to try every new tool immediately, but at least try to stay aware of major developments. A career in software engineering also dooms you to life-long learning in a very fast changing environment. This is no different. Agents are tools that work quite differently from what we're used to, and need cognitive effort and learning to wield effectively.
Waking up one day to realise you're now expected to work naturally in tandem with an AI agent but lack the experience is not a far-fetched scenario.
> Users should claim the output of LLMs as their own, for the following reason. LLMs are tools; tools can be used with varying degrees of skill; the output of tools (including LLMs) is a function of the user's skill; and therefore the output is attributable to and belongs to the user.
> Furthermore, we should use tools, including LLMs, actively and mindfully. We shouldn't switch off our brains and accept the output uncritically. We should iterate and improve as we go along.
I agree with you that the author seems to inappropriately convert differences in degree of skill into differences of kind.
One might say it's spec strumming.
He is absolutely right here, and I think in this article he has "shaped" the direction of future software engineering (which is already happening actually): we are moving closer and closer to a new way of writing code. But this time, for real. I mean that it will increasingly become the standard. Just as in the past an architect used to draw every detail by hand, while today much of the operational work is delegated to parametric software, CAD, BIM, and so on. The architect does not "draw less" because they know less, but because the value of their work has shifted. This is a concept we've repeated often in recent months, with the advent of Opus 4.5 and 5.2-Codex. But I think that here antirez has given it the right shape and also did well to distinguish it from mere vibecoding, which, as far as I'm concerned, are two radically different approaches.
* Does the spec become part of the repository?
* Does "true open source" require that?
* Is the spec what you edit?
This includes standard testing strategies, but also much more general processes
I think of it as steering a probability distribution
At least to me, this makes it clear where “vibe coding” sits … someone who doesn’t know how to express precise verification or feedback loops is going to get “the mean of all software”
There's actually a wealth of literature on defining levels of software automation (such as: https://doi.org/10.1016/j.apergo.2015.09.013).
Disagree.
So when there is a bug / outage / error, due to "automatic programming" are you ready to be first in line to accept accountability (the LLM cannot be) when it all goes wrong in production? I do not think that would even be enough or whether this would work in the long term.
No excuses like "I prompted it wrong" or "Claude missed something" or "I didn't check over because 8 other AI agents said it was "absolutely right"™".
We will then have lots of issues such as this case study [0] where everything seemingly looks fine at first, all tests pass but in production, the logic was misinterpreted by the LLM with a wrong keyword, [0] during a refactor.
[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
Absolutely yes. Automatic programming does not mean software developers are no longer accountable for their errors. Also because you can use AP in order to do ways more QA efforts than possible in the past. If you decide to just add things without a rigorous process, it is your fault.
I'm accountable for the code i push to production. I have all the power and agency in this scenario, so i am the right person to be accountable for what's in my PR / CL.
That is really about the least confusing part of the story.
To me code created like this smells like technical debt. When bugs appear after 6 months in production - as they do, if you didn't fully understand the code when developing it, how much time, energy and money will it cost to fix the problem later on?
More often than I like I had to deal with code where it felt like the developer did'nt actually understand what they were writing. Sometimes I was this developer and it always creates issues.
Replace "LLM" with "IDE" and re-read. The LLM is another tool. Of course tools can't be held responsible, the person wielding the tool is.
The contention here is that antirez doesn't think this is necessary anymore. 100% code gen, with the occassional "stepping in and tell the AI how to write a certain function"
Your position is more balanced and quite similar to https://mitchellh.com/writing/non-trivial-vibing
It is just that for some decades now it was possible to get away with poor or lacking specification, by continually incrementing by sprints, or pivoting, or whatever.
But, back in the day when systems analysts were still a person with very particular skills that might never code, specification was everything.
And now, the wheel turns, and the importance of good specification appears to be the defining factor once again, all of a sudden.
Even the most one shot prompt vibecoding is still getting high level intent from the person and then testing it in person. There is no "without being part of the process at all".
And from there its a gradient as to how much input & guidance is given.
This entire distinction he's trying to make here just doesn't make sense frankly. Trying to impose two categories on something that is clearly a continuous spectrum.
I embrace this new term.
"Vibe coding" is good for describing a certain style of coding with AI.
"Automatic programming" is what I get paid for in my 9-5, things have to work and they have to work correctly. Things I write run in real production with real money at stake. Thus, I behave like an adult and a professional.
Thank you 'antirez for introducing this language.
Everyone seems to want to invent a new word for 'programming with AI' because 'vibe coding' seems to have come to equate to 'being rubbish and writing AI slop'.
...buuuut, it doesn't really matter what you call it does it?
If the result is slop, no amount of branding is going to make it not slop.
People are not stupid. When I say "I vibe coded this shit" I do not mean, "I used good engineering practices to...". I mean... I was lazy and slapped out some stupid thing that sort of worked.
/shrug
When AI assisted programming is generally good enough not to be called slop, we will simply call it 'programming'.
Until then, it's slop.
There is programming, and there is vibe coding. People know what they mean.
We don't need new words.
It certainly quicker (and at times, more fun!) to develop this way, that is for certain.
Also I do feel like this is a very substantial leap.
This is sort of like the difference between some and many.
Your editor has some effect on the final result so crediting it/mentioning it doesn't really impact it (but people still do mention their editor choices and I know some git repo's with .vscode which can show that the creator used vscode, I am unfamiliar if the same might be true for other editors too)
But especially in AI, the difference is that I personally feel like its doing many/most work. It's literally writing the code which turns into the binary which runs on machine while being a black box.
I don't really know because its something that I am contradicted about too but I just want to speak my mind even if it may be a little contradicted on the whole AI distinction thing which is why I wish to discuss it with ya.
Waterfall made sense when changing code was expensive. Agile made sense when you couldn't know requirements upfront. But what if generating code becomes nearly free?
I've been experimenting with treating specs as the actual product - write the spec, let AI generate multiple implementations, throw them away daily. The spec becomes the persistent artifact that evolves, while code is ephemeral.
The surprising part: when iteration is cheap, you naturally converge on better specs. You're not afraid to be wrong because being wrong costs 20 minutes, not 2 sprints.
Anyone else finding that AI is making them more willing to plan deeply precisely because execution is so cheap that plans can be validated quickly?
Whatever you call it, for an experienced engineer to gain so much leverage in so little time while maintaining quality, it’s vibey and a ton of fun.
What does that even mean? You are a failed novelist who does not have ideas and is now selling out his fellow programmers because he wants to get richer.
Strongly disagree. This is a huge waste of currently scarce compute/energy both in generating that broken slop and in running it. It's the main driver for the shortages. And it's getting worse.
I would hate a future without personal computing.
Copyright protects human creations and the US Copyright Office has made it clear that AI output cannot be copyrighted without significant creative alterations from humans of the output after it is generated.
That belief has no basis at this point and it's been demonstrated not only that AI doesn't improve coding but also that the costs associated are not sustainable.
At the end of the day, you produce code for a compiler to produce other code, and then eventually run it.
It's called programming.
When carpenters got powertools, they didn't rename themselves automatic carpenters.
When architects started working with CAD instead of paper, they didn't become vibe architects, even though they literally copy-paste 3/5 of the content they produce.
Programming is evolving, there is a lot of senseless flailing because heads is spinning.
"OpenAI CFO Sarah Friar sketched a future in which the company's business models evolve beyond subscriptions and could include royalty streams tied to customer results." [1]
"Speaking on a recent podcast, Friar floated the possibility of "licensing models" in which OpenAI would get paid when a customer's AI-enabled work produces measurable outcomes." [1]
$30 a month or whatever amount of $$ per token does not justify the valuation of these companies. But you know what does? 5% of revenue from your software that their AI helped you to create. I can see world in which you must state you've used their AI to write code and you must use specific licenses to that code, which allows them part of your revenue.
[1] https://www.businessinsider.com/openai-cfo-sarah-friar-futur...
I posted yesterday about how I'd invented a new compression algorithm, and used an AI to code it. The top comment was like "You or Claude? ... also ... maybe consider more than just 1-shotting some random idea." This was apparently based on the signal that I had incorrectly added ZIP to the list of tools that uses LZW (which is a tweak of LZ78, which is a dictionary version of the back-reference variant by the same Level-Ziv team of LZ77, the thing actually used in Zip). This mistake was apparently signal that I had no idea what I was doing, was a script kiddie who had just tried to one shot some crap idea, and ended up with slop.
This was despite the code working and the results table being accurate. Admittedly the readme was hyped and that probably set this person off too. But they were so far off in their belief that this was Claude's idea, Claude's solution, and just a one-off that it seemed they not only totally misrepresented me and my work, but the whole process that it would actually take to make something like this.
I feel that perhaps someone making such comments does not have much familiarity with automatic programming. Because here's what actually happened: the path to get from my idea (intuited in 2013, but beyond my skills to do easily until using AI) was about as far from a 'one-shot' as you can get.
The first iteration (Basic LZW + unbounded edit scripts + Huffman) was roughly 100x slower. I spent hours guiding the implementation through specific optimization attempts:
- BK-trees for lookups (eventually discarded as slow).
- Then going to Arithmetic coding. First both codes + scripts, later splitting.
- Various strategies for pruning/resetting unbounded dictionaries.
- Finally landing on a fixed dict size with a Gray-Code-style nearest neighbor search to cap the exploration.
The AI suggested some tactical fixes (like capping the Levenshtein table, splitting edits/codes in Arithemtic coding), but the architectural pivots came from me. I had to find the winning path.
I stopped when the speed hit 'sit-there-and-watch-it-able' (approx 15s for 2MB) and the ratio consistently beat LZW (interestingly, for smaller dics, which makes sense, as the edit scripts make each word more expressive).
That was my bar: Is it real? Does it work? Can it beat LZW? Once it did, I shared it. I was focused on the bench accuracy, not the marketing copy. I let the AI write the hype readme - I didn't really think it mattered. Yes, this person fixated on a small mistake there, and completely misrepresented or had the wrong model of waht it actually took to produce this.
I believe that kind of misperception must be the result of a lack of familiarity with using these tools in practice. I consider these kind of "disdain from the unserious & inexperienced" to be low quality, low effort comments than essentially equate AI with clueless engineers and slop.
As antirze lays out: the same LLMs depending on the human that is guiding the process with their intuition, design, continuous steering and idea of software.
Maybe some people are just pissed off - maybe their dev skills sucked beofre AI, and maybe they still suck with AI, and now they are mad at everything good people are doing with AI, and AI itself?
Idk, man. I just reckon this is the age where you can really make things happen, that you couldn't make before, and you should be into and positive. If you are a serious about making stuff. And making stuff is never easy. And it's always about you. A master doesn't blame his tools.
You may not owe programming heroes or Linkedin inluencers better, but you owe this community better if you're participating in it.
On a more serious note, the technology & its use cases of AI are pretty dividing especially within software engineering. I would consider the fact that the financial incentives driving it and the what ~3 TRILLION $ invested in AI driving up some of this divide too.
It’s also sloppy and irresponsible. But hey, you can fake your work faster and more convincingly than ever before.
Call it slop coding.
I guess a large of that is that 1-2 years ago the whole process was much more non-deterministic and actually getting a sensible result much harder.
Sculding??
Rice by any other name??
The question is if you can have it all? Can you get faster results and still be growing your skills. Can we 10x the collective mind knowledge with use of AI or we need to spend a lot of time learning the old wayTM to move the industry forward.
Also nobody needs to justify what tools they are using. If there is a pressure to justify them, we are doing something wrong.
