Hi,I am on the mid-plan (paid) and using the web interface and in the last weeks the quality of Claude's output quality decreased dramatically. The model used is Sonnet 4.5 and my workflow is copy/pasting code and inspecting the diffs.It changes code it shouldn't change, even makes syntax errors (like a character different than the one provided in a source file).I know these LLMs change all the...
The method described here nowadays goes under the name of CFOP (Fridrich's page made it very popular, so in former days it was referred to the "Fridrich" method). It's still popular and got refined over the years, see this excellent comment in this thread: https://news.ycombinator.com/item?id=37430854
Another interesting topic is the question which (cross) color you choose: There are 6 possibilities (colors) and in the beginning even top solvers preferred one color (white is the most popular) to simplify recognition ("where are my pieces?").
Later, top solvers switched to "color neutral" solving, which in theory gives you more efficient crosses, as you can choose the color that gives you the shortest solution. There even seemed to be consensus that if you want to be at the top you had to be color neutral.
However even more recently this has been challenged and "dual color neutral" (only choosing between white and yellow) seems to be the way to go.
The reason is that during inspection (in competition you have 15 seconds to "inspect" the cube meaning to plan your solution (partly) ahead of time) the selection process to choose the best cross obviously takes some time. There's a trade-off between investing time to plan the cross (and additional blocks) in inspection and selecting the color that should be preferred.
Aren't they?
Twitter now disallows reading tweets, if users are not logged in.There are many threads on HN already and it's a big change in my opinion.I think it will be a turning point for better or worse. Maybe worse for Twitter (they might see short-term member increase but probably long term decline) but better for the world (people might be reconsidering where they publish their stuff).Do you think...
Small anecdote:When doing let's call it "mundane" coding, I am able to answer a big chunk of questions using ChatGPT only without having to use a search machine to "ask the internet". If you are experienced in the domain you can give useful hints to the LLM (e.g. you know which libraries to use, what's critical, etc.) and really get extremely useful answers. And I am not talking about common...
This will probably be popular in both the left-anarcho-liberal and right-wing parts of population (the first to oppose mega-corp capitalism the second to oppose governmental control and also mega-corp). But so it is agreed on by most parts of the population (maybe for different reasons), and might just become popular (again).
Interesting times ahead, that's for sure.
... or fat client era?At the moment Chat-GPT and other instruct-based models show what's possible with modern LLM models. Although most SOTA models need cloud-compute to predict, it's feasible to assume that they might work on beefy standard desktops in the next 5 to 10 years (IMHO).Now, historically we had: - thin-client main frame architecture (1970s - 1980s) - fat-client "home computers"...
Why not an established distribution like Fedora, Arch, Debian or Ubuntu?
> ChatGPT: [...] Instead, the punchline reveals that the man has actually quit drinking, which is unexpected and surprising, and effectively subverts the expectations that have been established in the setup of the joke.
But it only works because he is still drinking, I think ChatGPT does not understand anything here. If instead the joke would end with
> The man replies, "Oh, my brothers are fine. I just go hiking now myself."
I am pretty sure, ChatGPT will react similar.
In my experience (CNN based imagery segmentation) proven architectures (e.g. U-Net) performed similar with or without fine-tuning existing models (that have been mostly trained on imagenet, citiscapes, etc.) IF the domain was rather different.
At least in the field of imagery segmentation there is not much of a point in fine-tuning an off-the-shelf model on let's say medical imagery.
So maybe it's the same for the stable diffusion model. I don't see how some knowledge about the relationship between the prompt and given imagery describing that prompt should help this model map the prompt to a spectrogram of the given prompt.
Currently we are living in a thin-client epoch where many important compute stuff is done via SaaS (Google, Amazon, Wikipedia, StackOverflow, Dall-E, ...), but due to hardware and AI advancements it might be possible to compress much of stuff that matters into reasonable size (stable diffsion is around 4G and mobile phones might have 2T in the next 10 years).Though important stuff like- getting...
But there are many capable languages to develop memory save web applications (Haskell, Go, Rust, Java, Python, TypeScript, Elm, Clojure, ...), so why would one choose one that's not save?
So even the AI struggles to learn it. :p
A very inspiring story.
And I spent way less time learning Go than learning Haskell.
Yes, I'm referring to programmer circle related social networks (HN, r/programming, ...).
> It's quite surprising that aside from Fortran, C derivatives and Rust, no one gives a damn about performance, but when it's haskell, it suddenly becomes a problem.
Because performance problems are particularly hard to solve in Haskell due to its high level of abstraction.
> Is that some kind of strawman where if haskell doesn't deliver on being touted as the gift of God to programmers, then it's bad?
Yes, given the fact that it's praised to such a high degree, users might have extremely high expectations, but are likely to be underwhelmed if it does not solve their problems in a vastly superior way.
And I say that as someone, whose top-rated comment in this thread is more or less a Haskell rant.
It's a good programming languages, but being "magnitudes better" is absolutely not true in my experience.