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I don't really "get" the sweet-spot being targeted here. You don't get channels, goroutines, or gc, so aside from syntax and spatial memory safety you're not really inheriting much from Go. There is also no pathway to integrate with existing Go libraries.

Spatial memory safety is nice but it's the temporal safety that worries me most, in nontrivial C codebases.


I guess there is no point except Anton is having fun do it.

Looks to me like having the ability to write Go syntax and interop directly with C is the plus.

I do like Go's syntax but I can't help thinking the best language for C interop is C.

> I do like Go's syntax but I can't help thinking the best language for C interop is C.

SWIG[0] is a viable option for incorporating C code as well.

0 - https://swig.org/Doc4.4/Go.html#Go


I love how SWIG is still around! I first used it about 30 years ago to integrate with Perl, then later with Java.

Go's syntax is basically C tho lol

what's the benefit? for loops?


This seems anecdotal but with extra words. I'm fairly sure this is just the "wow this is so much better than the previous-gen model" effect wearing off.

I've always been a believer in the "post honey-moon new model phase" being a thing, but if you look at their analysis of how often the postEdit hooks fire + how Anthropic has started obfuscating thinking blocks, it seems fishy and not just vibes

I was in this camp as well until recently, in the last 2-3 weeks I've been seeing problems that I wasn't seeing before, largely in line with the issues highlighted in the ticket (ownership dodging, hacky fixes, not finishing a task).

Nope, there is a categorical degradation in quality of output, especially with medium to high effort thinking tasks.

What about the analysis evidences?

You mean the Claude output? The same claude that has "regressed to the point it cannot be trusted"?

What you saying the OP fabricated/hallucinated the evidence?

I'm just saying it's epistemically unrigorous to the point of being equivalent to anecdata.

How should one conduct such a rigourously reproducible experiment when LLMs by nature aren't deterministic and when you don't have access to the model you are comparing to from months ago?

Something like this: https://marginlab.ai/trackers/claude-code/ (see methodology section)

Kudos for the methodology. The only question I can come up with is that if the benchmarks are representative of daily use.

Anecdotal or not, we see enough reports popping up to at least elicit some suspion as to service degradation which isn't shown in the charts. Hypothesis is that maybe the degradation experienced by users, assuming there is merit in the anecdotes, isn't picked up by the kind of tracking strategy used.


It's not my methodology to be clear, but they have picked up actual regressions that happened in the past - e.g. https://news.ycombinator.com/item?id=46815013

I suspect you might be right but I don't really know. Wouldn't these proposed regressions be trivial to confirm with benchmarks?

Unless I missed it the writeup never identifies a causal bug, only things that made recovery harder.

This is obviously LLM output, but perhaps LLM output that corresponds to a real scenario. It's plausible that Claude was able to autonomously recover a corrupted fs, but I would not trust its "insights" by default. I'd love to see a btrfs dev's take on this!

This is also my first impulse. The second was, if this happened to me, I would not be able to recover it. All the custom c tool talk... If you ask Claude Code it will code something up.

Well that he recovered the disks is amazing in itself. I would have given up and just pulled a backup.

However, I would like to see a Dev saying: why didn't you use the --<flag> which we created for this Usecase


See this Reddit post for background: https://www.reddit.com/r/ClaudeAI/comments/1sdabux/hats_off_...

TLDR: The user got his filesystem corrupted on a forced reboot; native btrfs tools made the failure worse; the user asked Claude to autonomously debug and fix the problem; after multiple days of debugging, Claude wrote a set of custom low-level C scripts to recover 99.9% of the data; the user was impressed and asked Claude to submit an issue describing the whole thing.


Good to know that my claude-dar is still working.

I was assuming real scenario with heavy LLM help to recover. Would be nice for the author to clarify. And, separately, for BTRFS devs to weigh in, though I'd somewhat prefer to get some indication that it's real before spending their time.

An LLM wouldn't make a mistake like "One paragraph summary"

A decade or so ago, I had a clear idea of what a "native ui" should look and feel like, even if there were multiple routes to get there. I don't know any more.

Now that we have better ML, maybe we could take "link sentiment" into account too.

I don't know how good it was, but sentiment analysis was definitely a thing pre-ChatGPT.

It was pretty basic though, and even a frontier LLM might struggle to infer that OP is a negative-sentiment link, without sufficient context.

[dead]


Crawlers would need to use backlinks but also rank vector similarity to ensure the linked content matches the linked intent. Some kind of rainbow shades of how relevent the link is to the linkee and reverse.

You'd think, but very low quality AI-generated content regularly makes it to the HN front page, so it's just a numbers game.

so, like namespaces and cgroups?

Android kernel has the relevant kernel parameters disabled. It is entirely possible to run containers directly on android, but it requires enabled the relevant parameter (iirc no recompilation need, just a cmdline change). But this of course requires root.

I wish HN upvote data was public, I feel like I could build some kind of improved algorithm that reduces the vote weight of people who upvote slop.

Don't forget the 71 stars on github, and counting!

Oh wow, was 60 just a while ago. Guess the dead Internet theory is no longer just a theory.

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