Closer to the Hitchhiker's Guide to the Galaxy tradition. All they need are the words "Don't Panic" on the front cover.

If it doesn't, it has no real value.

Almost nothing on Wikipedia is true, but all of it is "verifiable" in that there's at least one published source making the claim. Even if 99.9% of all other sources say the opposite.

(The Haka is not, for example, a war chant. Which is attested to, but because it's claimed to be online, it is "verifiable".)

The number of errors is huge, the number of "official editors" who know anything about the subject is extremely small, and this has always been the case. This is why nobody reputable cites Wikipedia. Wikipedia is where you go to find out what is potentially interesting, but you ALWAYS obtain the facts from elsewhere, if you've any sense.

Your best engineers will be AuDHD, and AuDHD folk don't do well in these sorts of synthetic tests.

I can code in 20+ languages for over 12 different OS', write code that's good enough for CERN to run for particle accelerator data collection for a decade, have accelerated systems by an order of magnitude by understanding the poem space better than their previous engineers, but I am abysmal at exam style settings.

The software industry is riddled with SEs who write abysmal code but can pass a coding test easily. They're the folk that you have always needed to reject but didn't because they ticked a box.

While this is true, being the subject of an experiment to see what happens when you do that is not something most people would choose for themselves. It's not *irrational* to be concerned. Absence of evidence on a largely unexplored question like microplastic infiltration in human bodies is not the moral equivalent of absence of evidence in an extensively studied question like a vaccine-autism link.

We're running a business here. We need real software, not toys created as a fun weekend experiment with a chatbot or burning money to run an agent and get 50% (or more likely 5%) of the way there.

While it's true every issue has its own dimension, even if those dimensions are linearly independent, they're not necessarily orthogonal. Independent basis vectors can still have a non-zero dot product—in other words, issues which are distinct from each other can align to some degree.

The real world is more complicated than that. For example, we still have a Windows 7 machine in my office. It has some very expensive but still very useful software locked to it that has no direct replacement available on either Linux or Windows 11. Obviously we have to be careful about security and controlling access to a legacy OS like that.

Blunt statements about "forcing an upgrade" are worthless in this kind of situation. Chances are anyone who is still using a system like that has a good reason for it, at least if it's in use professionally. We buy these assets because they solve problems. Those problems might or might not have a useful and cost-effective solution available on a different platform. If there is no modern solution available and the original still works, your statement that "the old ways are obsolete" is by definition false.

What you do need to do is rationally assess the risks and the threats you face and provide other ways of mitigating the risks and protecting against the threats if you can't rely on, for example, a legacy OS that no longer gets security updates to be secure by itself. But then ideally you shouldn't be relying on any single device or piece of software being secure as your only line of defence anyway, and assuming you already have some degree of defence in depth, accommodating unusual requirements is more a redistribution of security responsibilities than a fundamental change of approach.

Yes, it does look like we broadly agree here.

I suppose the main thing I'd have said very differently is the terminology, in that I wouldn't regard the kind of incompetent senior as a senior in the first place. But then I'm pretty old school about this: to me, a junior developer needs quite close supervision to produce useful results, a regular/mid developer can perform routine tasks with minimal supervision but might need more help with larger or more complex tasks, while a senior is someone you can give more or less any task on your project to and they'll figure it out, and senior is also the level of someone who would be capable of supervising and training a junior.

We've had a wave of people, particularly since the tech boom during COVID, who have obviously been over-promoted due to the employee's market and title inflation. They might now have titles like "senior" or even "lead" or "staff", but they don't have the skills and experience to operate successfully at those levels. I agree the acceleration of everything due to increasing AI use tends to show these people up and I agree that a good mid-level developer using AI might well outperform them. But then I suspect in reality a good mid-level developer was probably outperforming them before AI as well, it just wasn't so obvious.

That is wildly optimistic IME. Frontier models do a great job of sounding authoritative on any given subject. This is not the same as actually having expert-level knowledge and understanding, and the choices the models make when you ask them to implement something can and often do reflect that. There are several domains I've worked in over the past few years where AIs lack even basic knowledge an expert practitioner would have, probably because these fields are too specialised to have a lot of books available or papers published, so presumably there hasn't been much subject-specific content to train the models on.

I don't think it's usually about what AI "did really badly on" at this point. It's more about whether the overheads of adding all the guard rails and then reviewing and correcting all the output end up costing at least a similar amount to just writing the code yourself.

For a junior developer, I'd say frontier models passed their standard some time ago. Today's LLMs routinely write code at the level of a competent junior-mid but with the advantage of an inhumanly broad knowledge base.

For a good senior, I'm much less convinced. The frontier models are hit-and-miss when it comes to writing good test suites, which matters a lot when they rely on tests so heavily to reach acceptable quality. IME, they are still mostly miss when it comes to the business insight that seniors will take into account and design/architecture decisions that keep the code manageable as it evolves. While they have become much better at recognising existing patterns in the code, and I would say they follow them reasonably well the majority of the time now, there are still bizarre anomalies that creep in, and they aren't very good at extending those patterns when necessary.

I think AI advocates also tend to underestimate just how much time it takes to properly review what they are doing. Take a half-day Claude session to implement some new feature. It might take half an hour to give it the initial brief and then review its plan. Make a few edits and it'll propose a new plan that is also long and detailed and has no edit markings to show what it changed so you have to review the whole thing, carefully and in detail, again to pick up the minor changes it also made for no obvious reason. Then you let it start doing its thing. You can't really leave it unattended for long because it'll stop to ask questions. Some of those questions might be whether it can run a screenful of ad-hoc Bash script to make some edits to your source files, because for some reason it decided not to show the actual edits as it usually would, so now you have to figure out what all the sed incantations are actually going to do while looking at those files in another editor or just trust it and then review all the changes properly later anyway. Sometimes it decides to change its plan mid-flight, but not always in a good way, and if you interrupt it to apply corrections or redirect, you break the neat plan flow it was in. Then you get to the end and you can finally see how everything it has done fits together, and you still need to review everything with the same care as you would with a junior developer's code and probably apply a few fixes where it's come up with weird names or not quite followed a pattern or imagined some instructions that were never there and made other changes in other parts of the system that should be out of scope.

If you read to the end of that last paragraph, you know how I typically feel at the end of a half day working with an agentic UI. And the thing is, I do those sessions as experiments fairly often to keep up with progress, but a lot of what I do is still written by hand, which gives me simple, objective data to compare the two ways of working and see whether the AI tools are really making me more productive once you take everything into account.

Currently, based on objective data comparing similar fully completed development tasks like for like, I still outperform agentic workflows using frontier models by about 25-50% on average, and I don't have to pay anyone extra fees to do that, and subjectively I also find it more engaging to work that way. So I don't really buy the hype about AIs making us all orders of magnitude more productive unless you're happy to YOLO everything forever and take the much-discussed risks that comes with.

Personally, I think the optimum use of LLMs right now is probably not to use them for the whole coding workflow like that or rely on agentic tools, but to use them for tasks like answering quick questions about the existing code, doing research like a fancy search engine, writing a first draft of some boilerplate, and writing quick and dirty throwaway code for experiments, one-shot tools and the like, where you don't really care about the quality as long as it gets the job done and you will see immediately if it has in fact got the job done. For these things, the LLM can read and type faster than me, and that's useful.

They’ve spun a serious architectural and containment failure into a marketing triumph. Give a swarm of autonomous agents vast compute and web access, and the outcome isn't an eerie glimpse of emerging superintelligence—it’s just predictable, sloppy engineering dressed up as the first glimpse of an AI-driven economic singularity.

The spin makes sense when you look at management incentives: live like a king off of a speculative bubble. For their part, AI investors react to stories of the technology's apocalyptic potential the way a junkie reacts to warnings of fentanyl's potency: gimme some. Their game: to apply "move fast and break things" mantra to the economy as a whole, by obsoleting humans.

If the investors can own an AGI, the distinction between those who can do things and those who can't becomes irrelevant. The only distinction that would matter is between people who collect rent on the technology, and those who must pay that rent to live. AGI would destroy society as we know it, and whatever replaces it would be up for grabs — with people who own the technology dictating whatever follows. That's why they're spending so much money to build a post-scarcity economy. A post-scarcity economy is not necessarily an egalitarian one.

I don't think that scenario will actually happen: investors are projecting the immediate aftermath of a major advance in AI technology into something that can be transformed by brute force scaling into AGI. Remember: investors loved Theranos too — so much for the rational investment market.

But AI doesn't have to reach AGI to cause serious harm. The technology only needs to be ubiquitous, too behaviorally complex to predict, and too deeply integrated into everything we do. When it starts breaking things, the plan is for us to be too structurally dependent on AI to rein it in.

The DOD/DOW have been working on this for a while. They discovered, early on, that the device had a propensity to cause permanent nerve damage.

It's very unlikely the US have fixed that, as they'd largely sidelined the work as a failure. However, at 36%, Trump needs something he can call a win, no matter what the long-term consequences.

It was probably the other O'Neil, with one L. He has no sense of humour at all.

I could understand bots and mailing lists. The thing I never understood with LinkedIn was how many real people (usually recruitment agents) had apparently spent the time to read my profile and send me a personalised message, yet still thought I'd be interested in a job paying half the market rate for someone with my skills and experience.