hathawsh

I thought the same until I had kids. I realized that kids generally want to learn, but they usually think what the teacher is telling them is not the best thing to be learning. No technology, not even AI, overcomes the student's ego; in fact, technology rarely even tries to address that issue. I think this often applies to adults too. Teachers continue to be necessary to help students achieve an appropriate level of humility to be able to learn.

You clearly have enough humility in your field to be able to soak up knowledge. That's great for you! I suggest your dedication is rare and teachers continue to be essential for society.

While I agree that a coding model (such as Opus) by itself tends to act very shallowly, when it's driven by a harness like Claude Code, the combination seems to be a far more general thing than a LLM. It's capable of consistently making excellent data structure and architectural choices over large code bases. It imitates thinking about anything and it can drive itself for hours.

Honestly, if I simply fed it a sense of presence (I would repeatedly tell it what's going on right now and ask it to react if it thinks it should), it would feel eerily like AGI.

I try to remember to say something like this: "This is an interesting solution, but I'm not confident we have a shared understanding of the problem we're solving. I have a few guesses, but I don't want to bias the discussion, so please restate the problem without suggesting a solution. If it's too difficult to explain, I can suggest what problems I think you might be running into." I wish I always remembered to say that. :-)

Thanks for the correction. In my own words, to commit to memory: once "the cat is out of the bag", the point is not that it's difficult to put the cat back in, the point is that we now know it's a cat and putting it back is not relevant.

"The cat is out of the bag" doesn't mean the cat is gone, it means the cat has freedom. I read the GP as meaning "no single company can currently control the trajectory of AI." That's not unfortunate -- that's great!

Last time I joined a discussion about US health care on HN (years ago), the picture looked bleak: secret chargemasters in every hospital, no realistic way for patients to compare options because the options are too complicated, and lots of people still falling through the cracks.

I can't help but think the picture looks very different now. There is growing awareness that chargemasters can not be secret. LLMs offer a way to overcome the complexity of choosing options, opening doors for real competition. I sincerely hope we can take advantage of this changed landscape to provide better care. Universal health care remains an interesting possible direction, but I now have hope that we can do a lot better within the existing laws.

That's fantastic. I have long, winding trails near me also and one of these days I also want to start prompting a coding agent on my bike with a headset. Do you recommend any particular type of headset?

Edit: never mind, I see you already suggested the Shokz OpenComm2 in another comment. Thanks!

Yes, but null pointers are so pervasive in C code that we really can't afford to put assertions everywhere. It's often better to let the app crash on violations.

Iroh looks very interesting!

How current is the PyPI package? https://pypi.org/project/iroh/

If I were doing a code review, I would probably accept the code either with or without the assertion. The context of curl_getenv() makes it clear that null is not acceptable. If the author of curl_getenv() had evidence that callers are frequently breaking the contract by passing null, then perhaps the assertion would help shed some light on violators. Otherwise, I would expect everyone to play by the rules, making the assertion unnecessary.

When I tell my coworkers to stop using AI to dress up their words, it's not because I care about human effort. The problem is that my coworkers often start with incorrect assumptions, and AI is good at amplifying bad assumptions and making them sound plausible. I have to spend extra time guessing at what the author originally wrote and then address the partly-hidden original points rather than what the AI generated. Give me your spelling errors, your grammar, your mumbles, your incoherent streams of thought, your doubt and uncertainty. Those things are extremely important, yet your robot obscures them.

Strangely, I've also observed that some customers respond very well to words dressed up by AI, even if the words oversimplify the truth. Now I'm working to understand why they want that. Are my customers not swimming in AI slop like the rest of us?

BTW, this doesn't mean I'm anti-AI. AI coding is an incredible superpower and I use it constantly, but it seems to me that AI coding works because code expresses the minutiae that is rightfully omitted from most other communication.

Sorry to break it to you, but on that timeline, the good things got poisoned. IBM enhanced Lisp with Enterprise Ready features like Spreadsheet Macro Builder, Microsoft took over development of Smalltalk and morphed it into BASIC 2.0, and the HURD community lost a bizarre copyright lawsuit. Fortunately for those folks, an intrepid hacker in the 90s saw some of the interesting ideas in MS-DOS and rebuilt it as LS-DOS. Today, most of their servers and mobile phones run LS-DOS or similar.

Your HN account is too new for me to be sure whether you're being sarcastic or not. Perhaps you know, or perhaps you don't, that all code is machine-translated, even assembly language. None of it is perfect, but it's not garbage. Today's AI merely provides a new level. It's a weird, non-deterministic level, but hiring an employee to write code for you is similarly non-deterministic.

These are also the markers of human journalists who write daily. Journalism is the reason AI acquired these habits. Gemini says this article is probably not generated by AI, particularly because it has original quotes.

https://gemini.google.com/share/ba48849a15a9

I actually agree with this. Product designers and product managers are often essential and sometimes they do up to 99% of the work of figuring out how something should work. To accomplish that, they often do things well outside the role of a software developer. On the other hand, in my experience, only someone with a software development mindset seems to be able to complete the last 1% (or 10%, or whatever) that reveals and resolves certain kinds of logic issues.

Isn't that interesting? The job of exploring a theory or model to such an extent that it can be expressed in computer code always seems to fall on the shoulders of a software developer. Other people can write specifications and requirements all day long, but until a software developer has tackled the problem, the theory probably hasn't been explored well enough yet to express clearly in computer code. It feels like software developers are scientists who study their customers' knowledge domains.

Is that actually true, though? Even though it's not really my job, I find myself debugging certificates and keys at least once a month, and that's after automating as much as possible with certbot and cloud certificates. PKI always seems to demand attention.

This is an amazing resource. It was difficult to appreciate what this resource was for until I tried to create my own boards based on an ESP32. It's not really difficult to build around ESP32, it's just that I don't know what I don't know. With starting points like these, I can start with a lot more confidence. Thank you!

You hinted at an aspect I probably haven't considered enough: The code I'm working on already has many well-established, clean patterns and nearly all of Claude's work builds on those patterns. I would probably have a very different experience otherwise.

I love the IDE skins analogy. Very true.

I'm on the $200/month max plan.

Sorry, no, and they're highly project specific anyway. I just started with the "/init" skill a few weeks ago and gradually improved it from there.

I'm either in a minority or a silent majority. Claude Code surpasses all my expectations. When it makes a mistake like over-editing, I explain the mistake, it fixes it, and I ask it to record what it learned in the relevant project-specific skills. It rarely makes that mistake again. When the skill file gets big, I ask Claude to clean and compact it. It does a great job.

It doesn't really make sense economically for me to write software for work anymore. I'm a teacher, architect, and infrastructure maintainer now. I hand over most development to my experienced team of Claude sessions. I review everything, but so does Claude (because Claude writes thorough tests also.) It has no problem handling a large project these days.

I don't mean for this post to be an ad for Claude. (Who knows what Anthropic will do to Claude tomorrow?) I intend for this post to be a question: what am I doing that makes Claude profoundly effective?

Also, I'm never running out of tokens anymore. I really only use the Opus model and I find it very efficient with tokens. Just last week I landed over 150 non-trivial commits, all with Claude's help, and used only 1/3 of the tokens allotted for the week. The most commits I could do before Claude was 25-30 per week.

(Gosh, it's hard to write that without coming across as an ad for Anthropic. Sorry.)

I wonder what the PGP signing concept does to thwart people who want to profit and don't care about the public good. It seems like anyone who attends a signing party can sell their key to the highest bidder, leading to bots and spammers all over again.

Yep, we're on the same wavelength.

Also add a PR reviewer bot. Give it authority to reject the PR, but no authority to merge it. Let the AIs fight until the implementation AI and the reviewer AI come to an agreement. Also limit the number of rounds they're permitted to engage in, to avoid wasting resources. I haven't done this myself, but my naive brain thinks it's probably a good idea.

I believe the GP post is saying that if we react to the new AI-enabled environment by arbitrarily strengthening IP controls for IP owners, the greatest benefactors will almost certainly be lawyer-laden corporations, not communities, artists, or open source projects. That seems like a reasonable argument.

It seems like the answer is to adjust IP owner rights very carefully, if that's possible. It sounds very hard, though.

A lot of code is "useless" only in the sense that no one wants to buy it and it will never find its way into an end user product. On the other hand, that same code might have enormous value for education, research, planning, exploration, simulation, testing, and so on. Being able to generate reams of "useless" code is a highly desirable future.

While Rust is excellent, you must acknowledge that Rust has issues with compilation time. It also has a steep learning curve (especially around lifetimes.) It's much too early to say Rust is the "final" language, especially since AI is driving a huge shift in thinking right now.

I used to think that I would never write C code again, but when I decided recently to build something that would run on ESP32 chips, I realized there wasn't any good reason for me to use Rust yet. ESP-IDF is built on C and I can write C code just fine. C compiles quickly, it's a very simple language on the surface, and as long as you minimize the use of dynamic memory allocation and other pitfalls, it's reliable.

Several months ago, just for fun, I asked Claude (the web site, not Claude Code) to build a web page with a little animated cannon that shoots at the mouse cursor with a ballistic trajectory. It built the page in seconds, but the aim was incorrect; it always shot too low. I told it the aim was off. It still got it wrong. I prompted it several times to try to correct it, but it never got it right. In fact, the web page started to break and Claude was introducing nasty bugs.

More recently, I tried the same experiment, again with Claude. I used the exact same prompt. This time, the aim was exactly correct. Instead of spending my time trying to correct it, I was able to ask it to add features. I've spent more time writing this comment on HN than I spent optimizing this toy. https://claude.ai/public/artifacts/d7f1c13c-2423-4f03-9fc4-8...

My point is that AI-assisted coding has improved dramatically in the past few months. I don't know whether it can reason deeply about things, but it can certainly imitate a human who reasons deeply. I've never seen any technology improve at this rate.