Why? People don't ask hammers to do much more than bash in nails into walls.
AI coding tools can be incredibly powerful -- but shouldn't that power be focused on what the tool is actually good at?
There are many, many times that AI coding tools can and should be used to create a "small program that already exists in multiple forms in the training data."
I do things like this very regularly for my small business. It's allowed me to do things that I simply would not have been able to do previously.
People keep asking AI coding tools to be something other than what they currently are. Sure, that would be cool. But they absolutely have increased my productivity 10x for exactly the type of work they're good at assisting with.
I think it's for a very reasonable reason: the AI coding tool salespeople are often selling the tools as something other than what they currently are.
I think you're right, that if you calibrate your expectations to what the tools are capable of, there's definitely. It would be nice if the marketing around AI also did the same thing.
And if this submission was an AI salesperson trying to sell something, the comment/concern would be pertinent. It is otherwise irrelevant here.
The parent specifically noted that generally "people keep asking AI coding tools to be something other than what they currently are."
There's an explanation for why that is happening, so I think it's relevant here.
Maybe this is possible. Maybe not.
However, it's a fantasy. Granted, it is a compelling fantasy. But its not one based on reality.
A good example:
"AI will probably be smarter than any single human next year. By 2029, AI is probably smarter than all humans combined.” -- Elon Musk
This is, of course, ridiculous. But, why should we let reality get in the way of a good fantasy?
Arguably that's already so. There's no clear single dimension for "smart"; even within exact sciences, I wouldn't know how to judge e.g. "Who was smarter, Einstein or Von Neumann?". But for any particular "smarts competition", especially if it's time limited, I'd expect Claude 4.5 Opus and Gemini 3 Pro to get higher scores than any single human.
Hear me out: let's say that generating a new and better compression algorithm is something that might take a dedicated researcher about a year of their life, and that person is being paid to work on it, in the industry or via a grant. Is there anyone who has been running Claude Code instances for a human-year in a loop with the instruction to try different approaches until it has a better compression algorithm?
Let's not disregard interesting achievements because they are not something else.
No one is propping up a multi-billion dollar tech bubble by promising hammers that do more than bash nails. As a point of comparison that makes no sense.
“It resembles a normal hammer but is outfitted with an little motor and an flexible head part which moves back and forth in a hammering motion, sparing the user from moving his or her own hand to hammer something by their own force and by so making their job easier”
With this context the example may support the quote, nail guns do make driving nails much faster and easier but that's all they do. You can't pull a nail with a nail gun and you can't use it for any of the other things that a regular hammer can do. They do 10x your ability to drive nails though.
On the other hand, LLMs are significantly more multi-purpose than a nail gun.
Because I keep wondering myself if AI is here and our output is charged up, then why am I keep seeing more of the same products but with an "AI" sticker slapped on top of them? From a group of technologists like HN and the startup world, that live on the edge of evolution and revolution, maybe my expectations were a bit too high.
All I see is the equivalent of a "look how fast my new car made me go to the super market, when I'm not too demanding on the super market I want to end up with, and all I want is milk and eggs". Which is 100% fine, but at the end of the day I eat the same omelette as always. In this metaphor, I don't feel the slightest behind, or have any sense of FOMO if I cook my omelette slowly. I guess I have more time for my kids if I see the culinary arts as just a job. And it's not like restaurants suddenly get all their tables booked faster just because everyone cooks omelettes faster.
>It's allowed me to do things that I simply would not have been able to do previously.
You're not the one doing them. Me barking orders to John Carmack himself doesn't make me a Quake co-creator, and even if I micromanage his output like the world's most toxic micromanager who knows better I'm still not Carmack.
On top of that, you would have been able to do previously, if you cared enough to upskill to the point where token feeding isn't needed for you to feel productive. Tons of programmers broke barriers, and solved problems that haven't been solved by anyone in their companies before.
I don't see why everyone claiming that they previously couldn't do something is a bragging point. The LLM's that you're using were trained by the Google results you could've gotten if you Google searched.
I wonder if AI coding tools might get out of this for some cases at least. Make an app that is clearly derivative but actually is optimized to do the thing it actually is supposed to do.
Harder with network effect apps but might be possible for others.
This is still pretty great!
Like the DVD catalogue software I was using[0] became subscription based, and I'm not paying 50€/year for that.
Just rewrote the bits that I need this afternoon with Claude. It definitely doesn't have the same features as the CLZ Movies app had, but it has all the ones I need specifically (adding movies easily, quickly seeing if I already own a movie).
And the same will happen to more and more SaaS style things unless they offer something unique a self-made and self-hosted one can't provide.
- https://highload.fun/tasks/15/leaderboard
- https://highload.fun/tasks/24/leaderboard
In both cases my score showed other players that there were better solutions and pushed them to improve their scores as well.
Don't get me wrong, AI is at least as game-changing for programming as StackOverflow and Google were back in the day. Being able to not only look up but automatically integrate things into your codebase that already exist in some form in the training data is incredibly useful. I use it every day, and it's saved me hours of work for certain specific tasks [0]. For tasks like that, it is indeed a 10x productivity multiplier. But since these tasks only comprise a small fraction of the full software development process, the rest of which cannot be so easily automated, AI is not the overall 10x force multiplier that some claim.
Until it decides to include code it gathered from a stackoverflow post 15 years ago probably introducing security related issues or makes up libraries on the go or even worse, tries to make u install libs that were part of a data poisoning attack.
As someone who frequently uses Claude Code, I cannot say that a year's worth of features/improvements have been added in the last month. It bears repeating: if AI is truly a 10x force multiplier, you should expect to see a ~year's worth of progress in a month.
They are by definition a biased source and should not be referenced as such.
I do however think he is not an actively dishonest source. When he says "In the last thirty days, I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5." I believe he is telling the truth.
That's what dogfooding your own product looks like!
That's obviously not going to happen, because AI tools can't solve for taste. Just because a developer can churn out working code with an LLM doesn't mean they have the skills to figure out what the right working code to contribute to a project is, and how to do so in a way that makes the maintainers lives easier and not harder.
That skill will remain rare.
(Also SQLite famously refuses to accept external contributions, but that's a different issue.)
Found that paperwork here: https://www.sqlite.org/copyright-release.html
I will make sure not to spread that misinformation further in the future!
Update: I had a look in fossil and counted 38 contributors:
brew install fossil
fossil clone https://www.sqlite.org/src sqlite.fossil
fossil sql -R sqlite.fossil "
SELECT user, COUNT(*) as commits
FROM event WHERE type='ci'
GROUP BY user ORDER BY commits DESC
"
I learned it from newspapers: papers that publish prompt and clear corrections when they publish mistakes are more credible than papers that don't acknowledge their errors.
> Contributed Code
> In order to keep SQLite completely free and unencumbered by copyright, the project does not accept patches. If you would like to suggest a change and you include a patch as a proof-of-concept, that would be great. However, please do not be offended if we rewrite your patch from scratch.
I realize that the section, "Open-Source, not Open-Contribution" says that the project accepts contributions, but I'm having trouble understanding how that section and the "Contributed Code" section can both be accurate. Is there a distinction between accepting a "patch" vs. accepting a "contribution?"
If you're planning to update this page to reduce confusion of the contribution policy, I humbly suggest a rewrite of this sentence to eliminate the single and double negatives, which make it harder to understand:
> In order to keep SQLite in the public domain and ensure that the code does not become contaminated with proprietary or licensed content, the project does not accept patches from people who have not submitted an affidavit dedicating their contribution into the public domain.
Could be rewritten as:
> In order to keep SQLite in the public domain and prevent contamination of the code from proprietary or licensed content, the project only accepts patches from people who have submitted an affidavit dedicating their contribution into the public domain.
More generally, I do think LLMs grant 10x+ performance for most common work: most of what people do manually is in the training data (which is why there's so much of it in the first place.) 10x+ in those domains can in theory free up more brain space to solve the problems you're talking about.
My advice to you is to tone down the cynicism, and see how it could help you. I'll admit, AI makes me incredibly anxious about my future, but it's still fun to use.
90-99% of an engineer's work isn't entirely novel coding that has never existed before, so by succeeding at what "already exists", it can take us to 10x-100x productivity.
The automation of all that work is groundbreaking in and of itself.
I think that, for a while into the future at least, humans will be relegated to generating that groundbreaking work, and the AI will increasingly handle the rest.
Here is how it works: I take the latest state of the art model, usually one of the two or three currently being hyped....and ask it to create a short document that teaches Java, Python, or Rust, in 30 to 60 min, complete with code examples. Then I ask the same model to review its own produced artifact, for correctness and best practices.
What happens next is remarkably consistent. The model produces a glowing review, confidently declaring the document “production ready”… while the code either does not compile, contains obvious bugs, or relies on outright bad practices.
When I point this out, the model apologizes profusely and generates a “fixed” version which still contains errors. I rinse and repeat until I give up.
This is still true today, including with models like Opus 4.5 and ChatGPT 5.2. So whenever I read comments about these models being historic breakthroughs, I can’t help but imagine they are mostly coming from teams proudly generating technical debt at 100× the usual speed.
Things go even worst, when you ask the model to review a Cloud Architecture....
I think if you want to make this a useful experiment you should use one of the coding assistants that can test and iterate on its code, not some chatbot which is optimized to impress nontechnical people while being as cheap as possible to run.
Is that how we call Opus 4.5 now? :-)
"I paid for the $100 Claude Max plan so you don't have to - an honest review" -https://www.reddit.com/r/ClaudeAI/comments/1l5h2ds/i_paid_fo...
I'm wondering if your test includes allowing the models to run their code in order to validate it and then fix it using the error output? Would you be willing to share the prompts and maybe some examples of the errors?
I haven't had many problems working in Claude Code even with full on "vibe coding". One notable recent exception was in writing integration tests for a p2p app that uses WebRTC, XTerm.js, and Yjs where it ran into some difficulty creating a testing framework that involved a headless client and a local MQTT broker where we had to fork a few child processes to test communication between them. Opus got bogged down working on its own so I stepped in and got things set up correctly (while chatting with Opus through the web interface instead of CC). The problem seemed to be due to overfilling the context since the test suite files were too long so I could have probably avoided the manual work by just having Opus break those up first.
I am happy as is tbh, not even looking for AGI and all. Just that the LLM be close enough to my thinking scale so that it does not feel "why am I talking with this robot".
Not either of the species of algorithms you've described, but still an advance.
1. Current LLMs do much better than produce "small programs that already exist in multiple forms in the training data". Of course the knowledge they use does need to exist somewhere in training data, but they operate at a higher level of abstraction than simply spitting out programs they've already seen whole cloth. Way higher.
2. Inventing a new compression algorithm is beyond the expectations of all but the the most wild-eyed LLM proponents, today.
This is going to destroy my home network, since I never moved it off the little Lenovo box sitting in my laundry room beside the Eero waypoint, but I’m out of town for three days, so
Granted, the seed of the idea was someone posting about how they wired pyiodide to Ace in 400 lines of JavaScript, so I can’t truly argue it’s non-trivial.
As a light troll to hackernews, only AI-written contributions are accepted
[Edit: the true inception of this project was my kid learning Python at school and trinket.io inexplicably putting Python 3 but not 2 behind the paywall. Alas, Securely will not let him and his classmates actually access it ]
That seems to be a strawman here, no? Sure, there exist people/companies claiming 10x-100x productivity improvements. I agree it's bullshit.
But the article doesn't seem to be claiming anything like this - it's showing the use of vibe-coding for a small personalized side-project, something that's completely valid, sensible, and a perfect use-case for vibe-coding.
This is the "promise" that was being sold here and in reality, we yet haven't seen anything innovative or even a sophisticated original groundbreaking discovery from an LLM with most of the claims being faked or unverified.
Most of the 'vibe-coding' uses here are quite frankly performative or used for someone's blog for 'content'.