exfalso

Can someone provide some insight into what use cases this is good for? Marketing material?

Because clearly when you hack and rootkit a box, the first thing you do is announce it to the public

why so few upvotes? i'm guessing everyone is busy firefighting...

what. I use Astra xhigh, sometimes max, never ran out of tokens on the 100$ thing. I'm using pi though which is by definition harder better faster stronger than claude code/codex.

pi+astra for me. Does absolute wonders. When openai starts to squeeze it's chinese models all the way

Come on, the trivializations start to sound quite unfounded now. Yes, a human was needed, but no, it wasn't a "collaboration"

It would have been a case of isolating exactly what functionality is needed and wiring that up with the actual requests. Not like a full pass-through proxy. This is what we've been doing in our company as well

To be fair, this is a classic prisoner's dilemma. If the others don't play along then there is no benefit at all, it's all negative for Anthropic.

No that's not what they did, they exposed jfrog raw. It would have been so extremely simple to gate services they need the llm to access... I mean, jfrog was not written with this kind of threat model in mind, and neither were a lot of other tools

What do you use as a European CDN atm?

It does happen, but it's very very rare. I met maybe 2 people in my career I could do this with.

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.