In 2017 I worked tirelessly with my colleagues to implement and replicate the first transformer paper.
Yesterday I left Opus 4.8 to go do some architecture research, with GPU access.
It replicated and trained a credible baseline. It implemented some ideas I'd been thinking about, and wrote custom CUDA kernels for them. It read and summarised dozens of related papers.
It has since run dozens of experiments, with minimal supervision. When a model is unstable it kills it, documents why, fires off a new configuration.
The realisation that frontier labs are doing this at scale with unlimited GPU and token budgets.
It actually scares me a bit. The realisation that the next big breakthroughs will only have light human involvement.
The prospect of recursive self improvement feels more to real to me all of sudden
I feel this. I've had a few tasks now where in honest retrospect I find myself asking "did that really speed me up". Its a bit demoralising cause not only do you waste time, you have a worse mental model of the resulting code and feel less sense of ownership over the result.
Brainstorming, ideation and small, well defined tasks where I can quickly vet the solution : these feel like the sweet spot for current frontier model capabilities.
(Unless you are pumping out some sloppy React SPA that you don't care about anything except get it working as fast as possible - fine, get Claude code to one shot it)
I recently made the shift to graphene from iOS and am mostly enjoying it.
The user profiles was slow to set up and not having shared filesystem between the user profiles creates friction. But I love that I can effectively sandbox my work apps, sandbox the Zuck apps etc, with different VPN profiles for each user.
Getting a burner google account (for gplay services) is a PITA if you are determined to get a clean slate from Googles tracking. Gplay is the only safe way to get certain apps at the moment, and make certain apps pass the device integrity checks.
I suspect one of the biggest barriers to mass adoption will be the fact that tap to pay doesn't work. IIUC apple/google pay are generally considered a privacy and security improvement over physical cards, since you don't give every merchant your actual card number.
Overall love the project and really nice to see such high quality open source software.
Yeah, worlds slowest and most in-efficient write-only database. And as soon as you need to interact with goods or services in the real world, then you still need trust anyway.
All these people harping on about: "Bro I just need to move my money without trusting anyone!, I just need a trust-less way to send currency bro!"
Trust is a good thing! Banks and financial middlemen aren't the devil. Look at how many TPS the visa network can do thanks to trust.
If it weren't for some minimum of social/institutional trust the whole of society would collapse anyway and your digital coins would finally converge to their true value (zero - or actually negative once you add in the externalities).
Fuck google for this. Awful decision. Guaranteed to be abused when Google or government despots decide that certain apps (or developers) aren't aligned with their interests.
Feeling very frustrated with the way the internet is going lately. This plus OSA + chat control. And compounded by the imperative for AI companies to keep hoovering up any and all data they can get their hands on, wiring it into "agentic" workflows and such.
You mean the guy that bans people from twitter for disagreeing with him? And has made a chatbot that spouts right-wing conspiracies in the name of being "anti-woke"?
HN still seems to value constructive criticism though, to an extent that is simply not possible on YouTube.
YouTube comment section is basically a cesspool of toxic positivity now, with absolutely no informational value at all.
I feel like this has been the case for a couple of years now, where nothing even slightly critical will be surfaced by whatever automated algorithm they are using to filter/boost content.
Yesterday I left Opus 4.8 to go do some architecture research, with GPU access.
It replicated and trained a credible baseline. It implemented some ideas I'd been thinking about, and wrote custom CUDA kernels for them. It read and summarised dozens of related papers.
It has since run dozens of experiments, with minimal supervision. When a model is unstable it kills it, documents why, fires off a new configuration.
The realisation that frontier labs are doing this at scale with unlimited GPU and token budgets.
It actually scares me a bit. The realisation that the next big breakthroughs will only have light human involvement.
The prospect of recursive self improvement feels more to real to me all of sudden