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Joined 1 year ago
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Cake day: June 7th, 2023

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  • I suppose having worked with LLMs a whole bunch over the past year I have a better sense of what I meant by “automate high level tasks”.

    I’m talking about an assistant where, let’s say you need to edit a podcast video to add graphics and cut out dead space or mistakes that you corrected in the recording. You could tell the assistant to do that and it would open the video in Adobe Premiere pro, do the necessary tasks, then ask you to review it to check if it made mistakes.

    Or if you had an issue with a particular device, e.g. your display, the assistant would research the issue and perform the necessary steps to troubleshoot and fix the issue.

    These are currently hypothetical scenarios, but current GPT4 can already perform some of these tasks, and specifically training it to be a desktop assistant and to do more agentic tasks will make this a reality in a few years.

    It’s additionally already useful for reading and editing long documents and will only get better on this end. You can already use an LLM to query your documents and give you summaries or use them as instructions/research to aid in performing a task.


  • Current LLMs are manifestly different from Cortana (🤢) because they are actually somewhat intelligent. Microsoft’s copilot can do web search and perform basic tasks on the computer, and because of their exclusive contract with OpenAI they’re gonna have access to more advanced versions of GPT which will be able to do more high level control and automation on the desktop. It will 100% be useful for users to have this available, and I expect even Linux desktops will eventually add local LLM support (once consumer compute and the tech matures). It is not just glorified auto complete, it is actually fairly correlated with outputs of real human language cognition.

    The main issue for me is that they get all the data you input and mine it for better models without your explicit consent. This isn’t an area where open source can catch up without significant capital in favor of it, so we have to hope Meta, Mistral and government funded projects give us what we need to have a competitor.



  • NFTs are stupid AF for most of the tasks people currently use them for and definitely shouldn’t be used as proof of ownership of physical assets.

    However, I think NFTs make a lot of sense as proof of ownership of purely digital assets, especially those which are scarce.

    For example, there are several projects for domain name resolution based on NFT ownership (e.g you look up crypto.eth, your browser checks that the site is signed by the owner of the crypto.eth NFT, then you are connected to the site), as it could replace our current system, which has literally 7 guys that hold a private key that is the backbone of the DNS system and a bunch of registrars you have to go through to get a domain. This won’t happen anytime soon but it is an interesting concept.

    Then I think an NFT would also be good as a decentralized alternative to something like Google sign in, where you sign up for something with the NFT and sign in by proving your ownership of it.

    In general though I find NFTs to be a precarious concept. I mean the experience I’ve had with crypto is you literally have a seed phrase for your wallet, and if it gets stolen all your funds are drained. And then for an NFT, if you click on the wrong smart contract, all your monkeys could be gone in an instant. There is in general no legal recourse to reverse crypto transactions, and I think that is frankly the biggest issue with the technology as it stands today.



  • Yeah there’s no way a viable Linux phone could be made without the ability to run Android apps.

    I think we’re probably at least a few years away from being able to daily drive Linux on modern phones with functioning things like NFC payments and a decent native app collection. It’s definitely coming but it has far less momentum than even the Linux desktop does.




  • I think this is downplaying what LLMs do. Yeah, they are not the best at doing things in general, but the fact that they were able to learn the structure and semantic context of language is quite impressive, even if it doesn’t know what the words converted into tokens actually mean. I suspect that we will be able to use LLMs as one part of a full digital “brain”, with some model similar to our own prefrontal cortex calling the LLM (and other things like vision model, sound model, etc.) and using its output to reason about a certain task and take an action. That’s where I think the hype will be validated, is when you put all these parts we’ve been working on together and Frankenstein a new and actually intelligent system.