Is it feasible that if we have a tokeniser that works on ELF (or PE/COFF) binaries, then we could have LLMs trained on existing binaries and have them generate binary code directly, skipping the need for programming languages?
I've thought about this a lot, and it comes down ultimately to context size. Programming languages themselves are sort of a "compression technique" for assembly code. Current models even at the high end (1M context windows) do not have near enough workable context to be effective at writing even trivial programs in binary or assembly. For simple instructions sure, but for now the compression of languages (or DSLs) is a context efficiency.
Wouldn't all binaries be in the training data, rather than the context? And output context could be in pieces, with something concatenating the pieces into a working binary?
Hashicorp has go-plugin: https://github.com/hashicorp/go-plugin. It does similar with support for net/rpc and grpc. With grpc, you could have the external process in Python. Unix domain sockets and TCP are supported. The framework handles spawning the process and managing it.
It is used extensively within hashicorp's products - nomad, packer etc.
The device maker controlling an app store made no sense always.
Its like saying the browser maker controls what websites you can visit.
We have so many efforts at keeping the web open, shouldn't we apply that to all platforms?
Good point on having developers in loop in a software business. Tesla might be a counter example, where it is often touted to be run like a "tech" company. Are developers in loop in the design of non-software stuff? (is chassis design an example? or maybe not?)
The other point on triangular communication with middle managers I think is known problem that Musk has publicly spoken about..
Not really though. That google demo looks like a single-pass feed-forward neural network trained to perform style transfer. The textures and colors get replaced while overall content is stayed the same.
This project seems to work by finding your image in the latent space of a GAN model, and then re-synthesizing a new image from that vector.
It's more like generating a whole new image which is targeting the overall look of an existing image, while jointly having optimized the generated image to look like it comes from a set of renaissance art.
Edit: on second thought, this tool might be running too quickly to be doing optimization to find an image in latent space. It might just be fancy vanilla style transfer done nicely. Hard to tell.
How bright should lights be to turn up on this? Why can't we see Aircraft carriers/Big merchant vessels or offshore oil wells or even research stations on the Antartic?
Is there a compsci oriented discussion forum with comaparable quality as HN in terms of discussions without the entrepreneurship bias? I would love to know and start following if a such a thing exists.
I still read Slashdot our of habit, but the technical content of the conversation is gone. It's mostly predestriant gripes about technopolitics and copyright/patent/microsoft/apple/google rantng.
reddit has a /compsci that is pretty good, and /programming gets pretty much all the same technical articles hn does without the entrepreneurship ones.
Are there any good subreddits for "patio11-esque" content? Not hard CS, not vc startups, not /r/SEO, but kinda the marketing/small-online-business thing?
LtU is extremely academic -- they expect Ph.D-level discussion in the submissions and comments. Which is wonderful, but mostly inaccessible to non--progrmaming-language-PhDs
