I recall seeing some pretty damning reports from a security pentester that was able to escape from a container on Azure and found the management controller for the service was years old with known critical unpatched vulnerabilities. Always been a bit sceptical of them since then
Where did the 4.8% number come from? Is it based on the validator stake? How does that compare to the number required to fork Bitcoin as a function of it's supply?
There was a vote after the DAO incident to roll back. 87% of those that voted voted yes (for the rollback), but only 5.5% of the total supply voted at all.
Where we've had some success is with heterogeneous agents with some cheap quantised/local models performing certain tasks extremely cheaply that are then overseen or managed by a more expensive model.
I've played with this type of thing and I couldn't justify it vs just using a premium model, which seems more direct and error proof. Cheap models in my experience could really consume tokens and generate cost
Struggling to find anything interesting or non-obvious about this article. You give a bunch of LLMs various parallelizable task and some models manage to do it well but others don't. No insights as to why. As someone with a distributed systems background the supposed 'insights' from distributed computing are almost trivial.
I think that's kind of the point.. Many people deploying these teams don't have a strong systems background, and they're empirically documenting where teams break down / making decisions inspired by human orgs rather than first principles. If people begin from a basic intuition for systems thinking, the tradeoffs should become obvious.
Interesting perspective re CXL synchronous API. Wouldn't things like OOO execution and speculation help with that? And anyway the latency is supposed to be comparable to NUMA latency, is that really such a deal breaker?
On a related question, I'm in the market to buy a new laptop for development and want to get something with good support for local models. What is a good recommendation in terms of GPU support etc? I currently have a Dell XPS 13. Should I just get a MacBook? Or are there good non-Mac options?
It stinks of LLM - sections with headers beginning with “The”, a lot of “it’s not just X, it’s Y” etc etc.
The content is good and interesting though. Just hard to wade through with all the thorny LLM bushes getting in the way.
Looks like the author had a draft with the core content and ideas and asked an LLM to embellish it. Maybe because author wasn’t confident in their writing skills? Whatever the reason, I’d honestly prefer something human-written.
Vagrant was a layer over virtualization, with hypervisors like virtualbox, kvm or vmware. The article mention virtualization and virtual machines several times, e.g. "unlike the virtual machine experience (which involved installing an entire operating system)".
For instance, deploying a complex Python application was hell, for lack of proper packaging. Using Vagrant was easy, but the image was huge (full system) and the software slow (full virtualization), among other problems. Containers like LXC and Docker were a bit easier to setup, much smaller, almost as performant as native packaging, and with a larger spectrum of features for sharing things with the host (e.g. overlay mounts).
Sure - I was just referring to the use of a Vagrantfile to configure the VM. The start of the article seemed to be pushing the Dockerfile itself as a big innovation.
You should read them as publicity to convince stupid politicians to continue to fund basic research when they are more inclined to go for tax cuts for billionaires. Annoying, but a necessary evil.
reply