Both are high-speed trains regardless of whether they are in motion or not.
High-speed is a descriptor of the type of train, not their current speed.
Just like a racing cars is still such even when stationary.
3,891 karma · joined January 9, 2016
High-speed is a descriptor of the type of train, not their current speed.
Just like a racing cars is still such even when stationary.
You rarely hear of such in Japan.
Many people hate or fear flying. Some might also just prefer the laid back, retro, travel style. Not all are in a mad rush to get somewhere.
But I guess the market might be too small for regularly scheduled trips.
Although, I guess rogue waves can appear anywhere deep and rough.
If the coding was nothing, every good marketing guy could go solo and start large software businesses. It does not happen. You mostly see successful technical founders, or usually a marriage of technical and business cofounders.
Finding a fit between viable business opportunities and the tech to address it is what's hard, and requires several skills: technical, marketing, communication, product design, etc.
https://news.ycombinator.com/item?id=50220976
https://news.ycombinator.com/item?id=50165791
https://news.ycombinator.com/item?id=50159827
The first links shows a basic failure of reasoning, which would have gone unnoticed if I was just vibe-coding.
The second link shows weak architectural sense and an inability to see the big picture.
The last link in particular is telling: the agents worked with me (over weeks to months) to implement a db abstraction layer without ever bringing up the fact that as it stands, that would have never worked with any indexes at all. I did not constrain them to my choices, but they never stopped me with that design. It took my eagle-eyed attention to their offhand comments to notice this problem, which then required a lot of rework to fix.
Absolutely not at all. Every single day that I work with Claude, there is abundant evidence that AI sucks at these. Ask me for examples if you need them.
Case in point: I am building a digital files archive organizer platform.
This is simply not the kind of product you do a quick prototype of, get it to market quickly, and iterate on based on any failures or issues.
There are an almost infinite number of ways to get the product wrong, in a technical, architectural, and business/market sense. It requires a lot of deep thinking and internal prototyping to even get at what would actually be useful and address the walled-content-silos problem tech vendors love to foist on us. it requires careful implementation to not mess up user data. It requires a ton of architectural and UX design to get a performant product that is easy to understand. I have mulled over architectural decisions for months and thrown them out totally after mounting evidence they were not right, without a single user yet (except myself).
Just quickly coding up the first ideas that come to mind in this space is almost guaranteed to be wasted effort, because I could try a 100 approaches in the market and all could be bad. Do not let the users do your work for you. Make sure you actually understand what you are building and why it is important. Then let the user's experiences tell you how to tweak it to be better.
What some of us are saying is that AI is poor at figuring out what to build, how best to architecture it, and what to avoid.
There is a method to it that produces good results (definitely better than the machines) with little effort. To start with, I pre-soak the clothes in good washing powder. I also do not let clothes pile up. I wash a bit at a time.
It's like anything goes in most human endeavors. Society is winging it; duct-taped solutions abound. Most people lack taste or a keen eye for quality, and only notice when there is something seriously wrong. Even then, politics and all sorts of other concerns skews how people respond to things. This is why we complain about AI slop in the fields we know about, while the public at large think AI is the best thing after sliced bread.
Faced with this, unless someone is really passionate about their work, they eventually kind of tune out, and then look for how to expend the least effort possible to get whatever (uncertain or politicized) reward they can in their endeavors.
It is a failure of imagination to think that there is no longer much challenge. But I guess all humans have a problem with tunnel vision, to various extents.
What is happening is this: the AI does not know when to be a more verbose in service of clarity, and when to be brief, again in service of clarity. There is no simple relationship between expression length and clarity; it all depends. And when they are verbose, it is usually a lot of words that actually say little.
That's the problem; there is some higher level reasoning they are incapable of when it comes to language usage.
I seems to remember a distinct time during which Claude for example became incomprehensible and weird with its writing, whereas before it was reasonably OK, even if still not that good
How do I know? It several times present deep architectural decisions to me, where each options may seem plausibly good. I rely on my deep architectural knowledge and experience, and knowledge of my vision for the product to steer it to the right option.
You would be well served to review the code. I don't usually need to do much of that since I work with models edit by edit, with every single line they write approved by me.
You would be surprised at all the errors even Claude Opus 5.5 commits. Not syntax or even basic logic errors, but more insidious errors that betray a lack of being able to see the bigger picture, or serious architectural errors.
If you want example, I have them in abundance. These models still need serious supervision. Unless you are spot checking the code deeply, you won't notice.
> My own parents are still relatively young and my in-laws are sceptical enough of other people that it’s currently safe but it won’t be so forever.
It is always interesting to me how people equate getting old with becoming dumb. I know there is definitely some age related decline that's inevitable, but if a person has grown up with computers, and maybe more importantly, has had a hacker and curious mindset while younger, it is not likely that they will suddenly become so naive and vulnerable, even when like 80.
What usually happens is that people lose interest in learning new things and keeping up. My own mom (who passed at 73) could never be convinced to use Whatsapp messaging, thinking it is something complicated for the young ones, and yet I currently know people in their mid 70s and even much older who are still as sharp as anything and use all the cool apps.
if (date == <some random future date>) { crash_and_burn_the_world() }
??
> The relation between them always has to be supplied, and supplying it is what the system’s logic is.
100% sure this is Claude's recent writing style at work
A few more of these, and you have a blob of software that has no recognizable shape, and does "everything", with no guardrails and gates on anything, whereas meaning arises from these.
The problem is not knowing when to stop, when to avoid implementing something. The AI will not stop you.
The skill and knowledge and intuition for what make good software cannot be offloaded, no matter how many times people write articles asserting that the machines have got it all figured out.
If anything, I enjoy programming more now with AI assistance, not vibe-coding. The depth and breadth of my work has increased greatly (yes, I still read and write a lot of code and documents, that the agents build on); I'm like a kid in a candy store.
Projects that would have seemed too daunting now seem feasible to tackle. I could even get started on implementing my dream language, or a knowledge management system for agents, or ... the possibilities are endless.
Now, my software engineering is for my own company, maybe that makes a difference. There is no one breathing down my neck to use more AI where it does not make sense, or to use AI this way or that. I am in the driver's seat, and I only delegate to AI what I need it for, and what it is good at. There is still much that is required of me that the AI cannot do, not anytime soon.
1. I created an authz lib for the app that enforces both at runtime and with types. I wanted to extend it to check not only the base DTO type shapes, but also DTOs with related record DTO properties. The context that is checked a triple of Role (e.g, admin, etc), Action (e.g., read, modify, insert, delete), and Entity.
Claude Opus initially tried to implement this enhancement by adding a role generic parameter to the DTOs (which kind of duplicates my utility type that accepts a DTO type and authz context and returns the narrowed type) and also, weirdly, wanted the role narrowing to apply only to nested DTOs. I had to throw out all that work, and ask it to enhance the existing utility auth types, and make that the authz narrowing applies to both the main DTOs and their related sub-DTOs always, at the same time.
2. I wanted to create a messaging system for the web app I'm developing. For example, where certain users with specific roles create specific entity records, admins are supposed to an email or SMS (depending in factor they registered with) plus an in-app message. These messages, especially the emails, are not just text, but formatted. Claude Opus wanted to embed formatting in the code that creates and send the messages. But this code works better when split up into various responsibilities: the message factory, the messages template, an the actual sender.
So I had it separate out the templates, implemented as JSX. Now if I want to change the templates, it is simple for me change just that, rather than hunting in some monolithic code to do that.
People keep saying this, and again and again I find it be false; a single session with Claude Opus is all it takes to debunk this, and yet people take it as given.
I have to wonder, have people drunk the vibe-coding cool-aid so much so that they are asserting the superiority of code they have never reviewed?