exfalso

I understand. So don't use them. There are plenty of OS alternatives

What are you talking about. You think service providers can backdoor aes-gcm? There will always be technology that they cannot get around. The only way to backdoor is to explicitly change the encryption.

There are ways to do it correctly. You just end up spending a lot of time conceptualizing and refining abstractions.

To me the issue is more that conceptualizing requires a certain state of mind. Before llms it was 10% hard thinking 90% implementing. Implementation was actually sort of a reward, it felt so good just being in the zone and fleshing out ideas.

Post llms I find myself walking up and down quite a lot, only doing the thinking. Now it's more like 40% thinking 60% reviewing plans/code. I haven't experienced flow state since. The thinking is fun but exhausting, the reviewing is just kind of annoying, especially as llms get into these weird failure modes. Before I could look at a bad piece of code and instantly tell what the author was thinking and why the thing doesn't work. Now I need to be a lot more careful because there is little code smell, but a lot of badly chosen abstractions.

Just exhausting...

They will most certainly cut subscription access, same as Anthropic. It is inevitable that they will go down the same lockin+squeeze route. I unsubscribed from Claude a couple of weeks ago, on gpt now. However, Openai will have to make a similar move.

At that point however open weight model providers will start to shine. All eyes on China.

Yeah that also sounds realistic, and actually there's evidence of this dulling effect from even before llms. The attention economy has been literally streamlining.. well, the road to death. And nobody is angry.

I can spin this in a weirdly positive light though. With fertility rates going down, life becoming less and less meaningful and simultaneously a small and decreasing group of people becoming extremely productive.. maybe humanity will finally stop exploiting the planet and start a sort of transition.

AI enhanced increased lifespan forest elves watching over nature. Mm I'd prefer that over soma. We are the heralds of The Great Ones

Agree on the knock-on effects. My prediction is deflation. Money will be worth more and more. As a consequence governments will have to step in to ensure inflation(with e.g. universal income), otherwise the economy stops.

But honestly I'm not sure this will be enough for people to spend on e.g. restaurants or activities or oh I don't know, children. I think this will imply a freezing or even stepping back on the Maslow pyramid, the majority of people consolidating in the middle.

What I'm mostly concerned about is not even economic, it's psychological. With nothing to do, people will not have purpose, and bored people are a gunpowder keg.

What's the point of that? That sounds like the most boring life. You want to rot away on a yacht? Private chef? Are you kidding?

Help family? Sure, although you don't need that much money for that. Friends? Ehh not very smart, just think about the changes in the friendships' authenticity.

My point was that there isn't anything I could do with that money, and neither can the vast majority of people in the world. So I would immediately try to pass it on to people who have better use for it

Wishing for 1B is completely nonsensical if you understand what kind of money that is.

Nonsense. What the hell would you do with 1B? Give it to charities maybe. Maybe set up an investment where dividends are paid to charity. Running out of ideas

That's.. not even close to being the case. It's literally a series of ambiguous questions and strategic decisions.

Non-ambiguous is like a first semester algorithms class in university.

I guess the new "Code's compiling!" is "Claude's lunching!"

Getting consistent 500s for any api call

It's to prevent distillation. Duh

It's because of simple FOMO of companies. If they don't "invest" in it they will be left behind. Which is true. However, the way they invest is equally (if not more) important. E.g. MS is a good example of how not to do it.

Regret? Of what? The tech is here. You won't slow it down by not using it. People need to either adapt by moving to more and more niche areas, or become the person to be retained when the efficiency gains materialize. We still don't have the proper methodology figured out, but people are working on it.

That said, I'd agree that people who currently claim 20x speedups will indeed be replaced.

It's failing when there is no data in the training set, and there are no patterns to replicate in the existing code base.

I can give you many, many examples of where it failed for me:

1. Efficient implementation of Union-Find: complete garbage result 2. Spark pipelines: mostly garbage 3. Fuzzer for testing something: half success, non-replicateable ("creative") part was garbage. 4. Confidential Computing (niche): complete garbage if starting from scratch, good at extracting existing abstractions and replicating existing code.

Where it succeeds: 1. SQL queries 2. Following more precise descriptions of what to do 3. Replicating existing code patterns

The pattern is very clear. Novel things, things that require deeper domain knowledge, coming up with the to-be-replicated patterns themselves, problems with little data don't work. Everything else works.

I believe the reason why there is a big split in the reception is because senior engineers work on problems that don't have existing solutions - LLMs are terrible at those. What they are missing is that the software and the methodology must be modified in order to make the LLM work. There are methodical ways to do this, but this shift in the industry is still in baby shoes, and we don't yet have a shared understanding of what this methodology is.

Personally I have very strong opinions on how this should be done. But I'm urging everyone to start thinking about it, perhaps even going as far as quitting if this isn't something people can pursue at their current job. The carnage is coming:/

Nope. Especially with these agents the thinking trace can get very large. No human will ever read it, and the agent will fill up their context with garbage trying to look for information.

I understand the drive for stabilizing control and consistency, but this ain't the way.

This is a terrible idea

Yes that's the idea. The expansion simply means that the window of migration will close. Once it's closed, your galaxy is cut off and will run out of fuel sooner than the high-density area.

There's a fun hypothesis I've read about somewhere, goes something like this:

As the universe expands the gap between galaxies widens until they start "disappearing" as no information can travel anymore between them. Therefore, if we assume that intelligent lifeforms exist out there, it is likely that these will slowly converge to the place in the universe with the highest mass density for survival. IIRC we even know approximately where this is.

This means a sort of "grand meeting of alien advanced cultures" before the heat death. Which in turn also means that previously uncollided UUIDs may start to collide.

Those damned Vogons thrashing all our stats with their gazillion documents. Why do they have a UUID for each xml tag??

Perhaps the out of job prediction is actually reversed. True, LLMs will become an efficiency increasing tool. But in terms of job security, doesn't that mean that if your whole job can be driven by an LLM then demand for that job decreases?

In other words, people claiming these high productivity increases may be the ones at actual risk. Why employ 3 people when 1 can write the prompts?

Maybe when AIs are able to say: "I don't know how this works" or "This doesn't work like that at all." they will be more helpful.

Funny you say that, I encountered this in a seemingly simple task. Opus inserted something along the lines of "// TODO: someone with flatbuffers reflection expertise should write this". I actually thought this was better than I anticipated even though the task was specifically related to fbs reflection. And it was because I didn't waste more time and could immediately start rewriting it from scratch.

I have the same experience and still use it. It's just that I learned to use it for simplistic work. I sometimes try to give it more complex tasks but it keeps failing. I don't think it's bad to keep trying, especially as people are reporting insane productivity gains.

After all, it's through failure that we learn the limitations of a technology. Apparently some people encounter that limit more often than others.

Interesting. I've had it fail on much simpler tasks.

Example: was writing a flatbuffers routine which translated a simple type schema to fbs reflection schema. I was thinking well this is quite simple, surely Opus would have no trouble with it.

Output looked reasonable, compiled.. and was completely wrong. It seemed to just output random but reasonable looking indices and offsets. It also inserted in one part of the code a literal TODO saying "someone who understands fbs reflection should write this". Had to write it from scratch.

Another example: was writing a fuzzer for testing a certain computation. In this case, there was existing code to look at (working fuzzers for slighly different use cases), but the main logic had to be somewhat different. Opus managed to do the copy paste and then messed up the only part where it had to be a bit more creative. Again, showing the limitation of where it starts breaking. Overall I actually considered this a success, because I didn't have to deal with the "boring" bit.

Another example: colleague was using Claude to write a feature that output some error information from an otherwise completely encrypted computation. Claude proceeded to insert a global backdoor into the encryption, only caught in review. The inserted comments even explained the backdoor.

I would describe a success story if there was one. But aside from throwing together simple react frontends and SQL queries (highly copy-pasteable recurring patterns in the training set) I had literally zero success. There is an invisible ceiling.

Exact same experience.

Here's what I find Claude Code (Opus) useful for:

1. Copy-pasting existing working code with small variations. If the intended variation is bigger then it fails to bring productivity gains, because it's almost universally wrong.

2. Exploring unknown code bases. Previously I had to curse my way through code reading sessions, now I can find information easily.

3. Google Search++, e.g. for deciding on tech choices. Needs a lot of hand holding though.

... that's it? Any time I tried doing anything more complex I ended up scrapping the "code" it wrote. It always looked nice though.

Highly recommend https://grml.org/zsh/

For nixos users: https://discourse.nixos.org/t/using-zsh-with-grml-config-and...

Super easy to setup, and works very well

Although it's sad, I have to agree with what you're alluding to. I think there is huge overhead and waste (in terms of money, compute resources and time) hidden in the software industry, and at the end of the day it just comes down to people not knowing how to write software.

There is a strange dynamic currently at play in the software labour market where the demand is so huge that the market can bear completely inefficient coders. Even though the difference between a good and a bad software engineer is literally orders of magnitude.

Quite a few times I encountered programmers "in the wild" - in a sauna, on the bus etc, and overheard them talking about their "stack". You know the type, node.js in a docker container. I cannot fathom the amount of money wasted at places that employ these people.

I also project that actually, if we adopt LLMs correctly, these engineers (which I would say constitute a large percentage) will disappear. The age of useless coding and infinite demand is about to come to an end. What will remain is specialist engineer positions (base infra layer, systems, hpc, games, quirky hardware, cryptographers etc). I'm actually kind of curious what the effect on salary will be for these engineers, I can see it going both ways.

Same experience here, using new models. Every time it's a disappointment. Useful for search queries that are not too specialized. That's it.

Wow one of the most insightful takes I've read in a long time, hidden in the depths of a HN thread.

The mechanism you talk about is all too real, and it can be semi-consciously exploited. A bit off-topic, but the way genocides happen is via a similar mechanism. A group is depicted as base, a disease, faults are attributed to it. It becomes a moral imperative to clean the body.

This feeling is thus rationalized, and people start to reinforce each other's conviction. It becomes a twisted status game: the more you lean into the mentality, the higher you rank. Often times the goal isn't even to "address" the original trigger, it's to show your peers your approval and distance yourself from "those people". They the immoral, we the moral.

Mostly feeling like a caveman. I've been trying and failing to use it productively since the start of the hype. The amount of time wasted could've been used for actual development.

I just simply don't get it. Productivity delta is literally negative.

I've been asking to do projects where I thought "oh, maybe this project has a chance of getting an AI productivity boost". Nope. Personal projects all failed as well.

I don't get it. I guess I'm getting old. "Grandpa let me write the prompt, you write it like this".