For a critical control plane component like this, I tend to prefer a constant work pattern [0], to avoid metastable failures [1], e.g. periodically pull the data instead of relying on notifications.

[0] https://aws.amazon.com/builders-library/reliability-and-cons...

[1] https://brooker.co.za/blog/2021/05/24/metastable.html

Comments URL: https://news.ycombinator.com/item?id=46334990

Points: 148

# Comments: 121

Makes a massive difference at high density and utilisation, with the standard cache/core performance can really degrade under load.

[0] https://www.amd.com/en/products/processors/technologies/3d-v...

[1] https://www.amd.com/en/products/processors/server/epyc/4th-g...

Current AI, and in particular RL-based, is already or will soon achieve super human performance on problems that can be - quickly - verified and measured.

So maths, algorithms, etc and well defined bugs fall into that category.

However architectural decision, design, long-term planning where there is little data, no model allowing synthetic data generation, and long iteration cycles are not so much amenable to it.

[0] https://www.jasonwei.net/blog/asymmetry-of-verification-and-...

Unfortunately it's not quite true, do to e.g. spacial prefetching [0]. See e.g. Folly's definition [1].

[0] https://community.intel.com/t5/Intel-Moderncode-for-Parallel...

[1] https://github.com/facebook/folly/blob/d2e6fe65dfd6b30a9d504...

You are aware that Gemini was trained on TPU, and that most research at Deepmind is done on TPU?

Comments URL: https://news.ycombinator.com/item?id=45766956

Points: 1

# Comments: 0

I had tinnitus for over 10 years. My tinnitus was not the usual ringing type, it was some sort of humming, low frequency noise. The frequency was not constant, it could vary. It could sometimes stop for 5-10 minutes, e.g. after a hot bath.

Went to see many specialists, tried everything, to no avail.

One day I started experiencing recurrent tension and light pain in my neck and shoulder blades, so I started doing some neck and shoulder blades stretches several times a day.

After a few weeks, the pain was gone, and I realised the tinnitus had stopped. This was maybe 2 years ago (I am still doing those exercises multiple times a day).

> Is hardware agnostic and uses TCP/IP to communicate.

So no RDMA? It's very hard to make effective use of modern NVMe drives bandwidth over TCP/IP.

> A logical shard is further split into five physical instances, one leader and four followers, in a typical distributed consensus setup. The distributed consensus engine is provided by a purpose-built Raft-like implementation, which we call LogsDB

Raft-like, so not Raft, a custom algorithm? Implementing distributed consensus correctly from scratch is very hard - why not use some battle-tested implementations?

> Read/write access to the block service is provided using a simple TCP API currently implemented by a Go process. This process is hardware agnostic and uses the Go standard library to read and write blocks to a conventional local file system. We originally planned to rewrite the Go process in C++, and possibly write to block devices directly, but the idiomatic Go implementation has proven performant enough for our needs so far.

The document mentions it's designed to reach TB/s though. Which means that for an IO intensive workload, one would end up wasting a lot of drive bandwidth, and require a huge number of nodes.

Modern parallel filesystems can reach 80-90GB/s per node, using RDMA, DPDK etc.

> This is in contrast to protocols like NFS, whereby each connection is very stateful, holding resources such as open files, locks, and so on.

This is not true for NFSv3 and older, it tends to be stateless (no notion of open file).

No mention of the way this was developed and tested - does it use some formal methods, simulator, chaos engineering etc?

[0] https://arxiv.org/abs/1901.01930

But generally the idea is that it's, you need some notion of reward, verifiers etc.

Works really well for maths, algorithms, amd many things actually.

See also this very short essay/introduction: https://www.jasonwei.net/blog/asymmetry-of-verification-and-...

That's why we have IMO gold level models now, and I'm pretty confident we'll have superhuman mathematics, algorithmic etc models before long.

Now domains which are very hard to verify - think e.g. theoretical physics etc - that's another story.

Of course we can, this is a non issue.

See e.g. AlphaZero [0] that's 8 years old at this point, and any modern RL training using synthetic data, e.g. DeepSeek-R1-Zero [1].

[0] https://en.m.wikipedia.org/wiki/AlphaZero

[1] https://arxiv.org/abs/2501.12948

Comments URL: https://news.ycombinator.com/item?id=44894413

Points: 1

# Comments: 0

[0] https://x.com/alexwei_/status/1946477742855532918

At this point, I think it can only be explained by ignorance, bad faith, or fear of becoming irrelevant.

[0] https://x.com/alexwei_/status/1946477742855532918

But the next step is obviously increased formalism via formal methods, deterministic simulators etc, basically so that one could define an environment for a RL agent.

Just looking at what happened with chess, go, strategy games, protein folding etc, it's obvious that pretty much any field/problem that can be formalised and cheaply verified - e.g. mathematics, algorithms etc - will be solved, and that it's only a matter of time before we have domain-specific ASI.

I strongly encourage everyone to read about the bitter lesson [0] and verifier's law [1].

[0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

[1] https://www.jasonwei.net/blog/asymmetry-of-verification-and-...

HBM is also very expensive.