Thank you! The idea is not completely mine, I have to give thanks to Abhinav Arora who had this idea initially during the ADCx music hackathon. Kudos to him! Also love the phrase Gradus ad Parnassum! Maybe this should be the motto of contrapunk :)
How are you finding rust for audio development? I have a background in pro audio, and both for the audio and GPU render threads, I used a lot of arena allocators and ring buffers. Do you find yourself fighting with rust's strict semantics?
This is callback heavy audio code so this was the bigger problem for me mainly and learning about lifetimes was a pain initially. cpal's stream callback wants 'static which means you can't just pass references around. You end up using channels (crossbeam / std::sync::mpsc) between the audio thread and everything else. Once I structured around that it got smoother. I also got a lot of help from AI to understand and reimplement a lot of the parts for this as you can tell from the commit messages.
I guess it's possible that Russia and/or China delivered some hardware to the Iranians. Doesn't seem far fetched given the low international support for this "excursion". Both countries benefit from a US quagmire.
The irony of an IP scraper on an absolutely breathtaking, epic scale getting its secret sauce "scraped" - because the whole app is vibe coded (and the vibe coders appear to be oblivious to things like code obfuscation cuz move fast!)...
And so now the copy cats can ofc claim this is totally not a copy at all, it's actually Opus. No license violation, no siree!
It's fucking hilarious is what it is, it's just too much.
The irony of an IP scraper on an absolutely breathtaking, epic scale getting its secret sauce "scraped" - because the whole app is vibe coded (and the vibe coders appear to be oblivious to things like code obfuscation cuz move fast!)...
And so now the copy cats can ofc claim this is totally not a copy at all, it's actually Opus. No license violation, no siree!
It's fucking hilarious is what it is, it's just too much.
Interesting article; I've always been fascinated and intimidated by FPGA programming - it's one of the few remaining "dark arts" of software engineering.
> VHDL’s delta cycle algorithm is its crown jewel. It gives you built-in determinism. Let us treasure it - Verilog doesn’t have anything like it. At the same time, you will agree with me that there is nothing too complicated about the concept.
> The deal was always simple: search engines had permission to crawl sites because they were going to be sending users to those sites. If they're hitting your site half a million times for every one user they send to your site, all they're giving you is higher costs.
Can you share what aspects of the design you (and Stroustroup) aren't happy with? Stroustroup has a tendency of being proven right, with 1-3 decade lag.
Certainly we can say that Bjarne will insist he was right decades later. We can't necessarily guess - at the time - what it is he will have "always" believed decades later though.
You made me laugh!...Bjarne indeed can't be accused of being a modest man. And by some accounts, he's quite a political animal.
But in fairness, when was D&E first published? Argued for auto there, long before their acceptance. Argued for implicit template instantiation - thank god the "everything-must-be-explicit" curmudgeons were vanquished there, too.
He's got a pretty good batting average - certainly better than Herb Sutter.
I'll have a stab at this. I'll start with an attempt at justifying the remark that an agent which is a good coder will be good at other tasks.
1. Coding is, as a technical endeavour, relatively difficult (similarly for mathematics). So a model which performs well on this task can be expected to easily handle also-technical-but-slightly-easier tasks, like understanding (musical) harmony theory or counterpoint -- for much the same reason that human programmers/mathematicians/scientist don't struggle to understand those "easier" theories.
2. Reinforcement learning augments a base models ability to excel in something else that's "difficult", namely to "look ahead" and plan multiple steps in advance. That's literally how the training algorithm works, generating multiple paths at once, and rewarding intermediate steps in those paths which succeed in attaining the goal. And that skill, too, is extremely useful in other domains. An AI agent which learns that to break a problem into sub-problems, and then tackle each in turn methodically -- it stands to reason that it can apply that to, say, a business plan.
Note: 1 & 2 are not independent, nor are frontier models' excellence in these domains magical: it ultimately boils down to the availability of massive datasets (in particular for coding) and totally objective metrics (in the case of mathematics: solved math problems). That's the key ingrediant for reinforcement learning to be so effective.
So: the skills are transferrable because they're difficult, and require lots of planning. That models are so good at them is a fluke, and in a parallel world where humans created git repo after git repo of business plans, it might be that which we lean on to teach a reinforcement learning algorithm how to "reason" and "plan".
Now let's turn our attention to the "synergies" aspect, which I agree with. Let's say your agentic model, which is already excellent at reasoning and planning, acquires a new or improved capability which allows it to search the domain space, calculate, etc. much better than before -- this capability can now bear upon the plan, or be factored into the plan. For example, the model might be able to say "I don't need to worry about this particular subproblem for now; I can rely on my "mathematica" capability to deal with it when I absolutely need."
Or to put it differently: monkeys, like humans, are able to use (rudimentary) tools. They'll take a rock, and use it to crack open a coconut (or whatever). But a human being, with far superior reasoning and planning abilities, takes that tool, and uses it to make an even better tool -- and the result after many iterations of this process is civilization as we know it, while monkeys are still stuck trying to crack open nuts with rocks.
In my experience, an indicator that my interlocutor is (possibly) lying/BSing, when challenged for an explanation for something they did, is that they provide a list of reasons. The person who's telling the truth just gives one.
