Same with remote attestation. Not all implementations are actually secure. But hopefully over time those security bugs can be ironed out and the cost to extract a key be made infeasable.
You are using it wrong, or are using a weak model if your failure rate is over 50%. My experience is nothing like this. It very consistently works for me. Maybe there is a <5% chance it takes the wrong approach, but you can quickly steer it in the right direction.
A lot of people are getting good results using it on hard things. Obviously not perfect, but > 50% success.
That said, more and more people seem to be arriving at the conclusion that if you want a fairly large-sized, complex task in a large existing codebase done right, you'll have better odds with Codex GPT-5.2-Codex-XHigh than with Claude Code Opus 4.5. It's far slower than Opus 4.5 but more likely to get things correct, and complete, in its first turn.
I think a lot of it comes down to how well the user understands the problem, because that determines the quality of instructions and feedback given to the LLM.
For instance, I know some people have had success with getting claude to do game development. I have never bothered to learn much of anything about game development, but have been trying to get claude to do the work for me. Unsuccessful. It works for people who understand the problem domain, but not for those who don't. That's my theory.
It works for hard problems when the person already solves it and just needs the grunt work done
It also works for problems that have been solved a thousand times before, which impresses people and makes them think it is actually solving those problems
Which matches what they are. They're first and foremost pattern recognition engines extraordinaire. If they can identify some pattern that's out of whack in your code compared to something in the training data, or a bug that is similar to others that have been fixed in their training set, they can usually thwack those patterns over to your latent space and clean up the residuals. If comparing pattern matching alone, they are superhuman, significantly.
"Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape. Their ability to pattern match makes reasoning seem more powerful than it actually is. If your bug is within some reasonable distance of a pattern it has seen in training, reasoning can get it over the final hump. But if your problem is too far removed from what it has seen in its latent space, it's not likely to figure it out by reasoning alone.
Have you tried Windows 11? The WSL2 integration works really well. And the work that is being done in regards to safe vms so games can move away from kernel anticheat is also exciting.
As someone that was really into WinRT, pity that the whole UWP stack went bust, it was so mismanaged that outside Windows team themselves no one else cares any longer.
As was the case with Windows 7, I'll move off of Windows 10 when they pry it from my cold, dead hands (or some new hardware is no longer compatible). Each "upgrade" from Microsoft is a regression.
I have to run several windows debloat tools and powershell scripts to both remove AI and spyware, apps that I do not want or need, and to also force windows 11 to not reinstall the apps and spyware that I have removed.
It's fine as long as you don't mind Microsoft recording everything you do, every game you play, every keystroke you make, all for the purpose of selling to other businesses so they can invade your life and sell things to you, and for them shoving their terrible microsoft software and their microsoft preferred way of doing things, not taking no for an answer, and even when you forcibly pry their invasive shitty fingers out of your hardware they just force them right back in on an update or a random reboot.
Fuck Windows 11. 10 was pretty shit, too but at least once you ran scripts to remove the microsoft bs from it it stayed out.
That's a false analogy. Product managers, designers, API implementers, kernel developers, etc. all understand what they're building and how that fits into a larger picture.
They may know the area they are responsible for, but they don't know all of the details of everything else and just have to trust that other people are doing the right thing and following contracts correctly. It doesn't require anyone to have full global understanding. Having local experts is good enough.
The point of school for me was to get a degree. 99% of the time at school was useless. The internet was a much better learning resources. Even more so now that AI exists.
I graduated about 15 years ago. In that time, I’ve formed the opposite opinion. My degree - the piece of paper - has been mostly useless. But the ways of thinking I learned at university have been invaluable. That and the friends I made along the way.
I’ve worked with plenty of self taught programmers over the years. Lots of smart people. But there’s always blind spots in how they approach problems. Many fixate on tools and approaches without really seeing how those tools fit into a wider ecosystem. Some just have no idea how to make software reliable.
I’m sure this stuff can be learned. But there is a certain kind of deep, slow understanding you just don’t get from watching back-to-back 15 minute YouTube videos on a topic.
I think it depends on how they were self taught. If they just went through a few tutorials on YouTube and learned how to make a CRUD app using the shiny tool of the week, then sure. (I acknowledge this is a reduction in self-teaching — I myself am self-taught).
But if they actually spent time trying to learn architecture and how to build stuff well, either by reading books or via good mentorship on the job, then they can often be better than the folks who went to school. Sometimes even they don't know how to make software reliable.
I'm firmly in the middle. Out of the 6 engineers I work with on a daily basis (including my CTO), only one of us has a degree in CS, and he's not the one in an architecture role.
I do agree that learning how to think and learn is its own valuable skill set, and many folks learn how to do that in different ways.
> But if they actually spent time trying to learn architecture and how to build stuff well, either by reading books or via good mentorship on the job, then they can often be better than the folks who went to school.
Yeah I just haven’t seen this happen. I’ve seen plenty of people graduate who were pretty useless. But … I think every self taught programmer I’ve worked with had meaningful gaps in their knowledge.
They’d spend a week in JavaScript to save them from 5 minutes with C or bash. Or they’d write incredibly slow code because they didn’t know the appropriate algorithms and data structures. They wouldn’t know how to profile their program to learn where the time is being spent. (Or that that’s even a thing). Some would have terrible intuitions around how the computer actually runs a program, so they can’t guess what would be fast or slow. I’ve seen wild abstractions to work around misunderstandings of the operating system. Hundreds of lines to deal with a case that can’t actually ever happen, or because someone missed the memo on a syscall that solves their exact problem. There’s also hairball nests of code because someone doesn’t know what a state machine is. Or how to factorise their problem in other ways. One guy I worked with thought the react team invented functional programming. Someone else doesn’t understand how you could write programs without OO inheritance. And I’ve seen so many bugs. Months of bugs, that could be prevented with the right design and tests.
I’ve worked with incredibly smart self taught programmers. Some of the smartest people I’ve ever worked with. But the thing about blind spots is you don’t know you have them. You say you’re self taught, and self taught people can be better than people who went to school. In limited domains, yeah. Smart matters a lot. But you don’t know what you don’t know. You don’t know what you missed out on. And you don’t know what problems in the workplace you could have easily solved if you knew how.
Yeah, I agree, but not knowing what you don't know applies to almost everyone in every skill, not just programming. I acknowledge I have gaps in my knowledge. But it's because of those gaps that I am always trying to supplement my knowledge by studying different data structures, different patterns for solving problems, different algorithms. I don't aim for complete mastery. I aim for basically "what can I add to my bag of problem solving tools." I concede that because the barrier to entry is low, stories similar to your anecdotes are probably quite common in most self-taught programmers. I think this just speaks to the necessity of rigor during the interview process. Like, does the candidate just know how to build features, or do they know how to design fail-proof systems?
Also, to clarify, I'm not arguing that self-taught vs CS grad is mutually exclusive to smart/not smart. There are plenty of not-smart self-taught engineers and plenty of smart grads.
> In limited domains
I'd argue that many, if not most, teams operate in limited domains.
> I think this just speaks to the necessity of rigor during the interview process.
That gets expensive, fast. There's just so much to cover already, between communication skills, programming skills, debugging skills, architecture / "whiteboarding problems", data structures and algorithms, general problem solving ("interview problems"). A job interview can never be a fully rigorous test of someone's actual skills. Most don't cover even a fraction of that stuff already.
> I'd argue that many, if not most, teams operate in limited domains.
It depends what you consider yourself responsible for. If you think of your job (or your team's job) as shipping features X, Y and Z within this react based web app, then sure - you operate in a limited domain. But if your job is "solve the user's actual problems" then things can get pretty broad, pretty fast. Sometimes you write code. Sometimes you're debugging a hard problem. Or talking to the users. Or identifying and tracking down a performance regression. Or writing an issue for a bug in 3rd party code. Or trawling through MDN to figure out a workaround to some browser nonsense. Or writing reliable tests, or CI/CD systems. And so on.
Its only really junior engineers who have the luxury of a limited scope.
I haven't heard of self taught programmers binging 15 minute YT videos. I can't recall the last time I did myself.. aside from conference talks and such its probably been at least 5 years since I watched something explaining things in the realm of programming.
>I’ve worked with plenty of self taught programmers over the years. Lots of smart people. But there’s always blind spots in how they approach problems.
I've worked with PhDs on projects (I'm self-taught), and those guys absolutely have blind spots in how they approach problems, plenty of them. Everyone does. What we produce together is better because our blind spots don't typically overlap. I know their weaknesses, and they know mine. I've also worked with college grads that overthink everything to the point they made an over-abstracted mess. YMMV.
>you just don’t get from watching back-to-back 15 minute YouTube videos on a topic.
This is not "self taught". I mean maybe it's one kind of modern-ish concept of "self taught" in an internet comment forum, but it really isn't. I watch a ton of sailing videos all day long, but I've never been on a sailboat, nor do I think I know how to sail. Everyone competent has to pay their dues and learn hard lessons the hard way before they get good at anything, even the PhDs.
For a motivated learner with access to good materials, schools provide two important things besides that very important piece of paper:
1. contacts - these come in the form of peers who are interested in the same things and in the form of experts in their fields of study. Talking to these people and developing relationships will help you learn faster, and teach you how to have professional collegial relationships. These people can open doors for you long after graduation.
2. facilities - ever want to play with an electron microscope or work with dangerous chemicals safely? Different schools have different facilities available for students in different fields. If you want to study nuclear physics, you might want to go to a school with a research reactor; it's not a good idea to build your own.
To extend 2. facilities, my experience had a - somewhat older and smaller - supercomputer that we got to run some stuff on.
And I'd argue for:
3. Realisation of the scope of computing.
IE Computers are not just phones/laptop/desktop/server with networking - all hail the wonders of the web... There are embedded devices, robots, supercomputers. (Recent articles on HN describe the computing power in a disposable vape!)
There are issues at all levels with all of these with algorithms, design, fabrication, security, energy, societal influence, etc etc - what tradeoffs to make where. (Why is there computing power in a disposable vape?!?)
I went in thinking I knew 20% and I would learn the other 80% of IT. I came out knowing 5 times as much but realising I knew a much smaller percentage of IT... It was both enabling and humbling.
But you can also meet experts at a company and get access to a company's machinery. To top it off the company pays you instead of you paying the school.
This is not my experience at all. Claude will ask me follow up questions if it has some. The claim that it goes full steam ahead on its original plan is false.
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