I think your job in your current form is likely in danger.

SOTA Foundation Models like GPT4 and Gemini Ultra can write code, execute, and debug with special chain of thought prompting techniques, and large acale process verification on synthetic data and RL search for correct outputs will make this 10x better. The silver lining to this is that I expect this to require an absolute shit ton of compute to constantly generate LLM output hundreds of times for each internal prompt over multiple prompts, requiring immense compute and possibly taking longer than an ordinary software engineer to run. I suspect early full stack developer LLMs will mainly be used to do a few very tedious coding tasks and SWEs will be cheaper for a fair length of time.

I expect it will be 2-3 years before this happens, so for that short period I expect workers to be “super-productive” by using LLMs in the coding process, but I expect the crossover point when the LLM becomes better is quite soon, perhaps in the next 5 years as compute requirements go down.

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.

Yeah, I think Nix is a good concept but I feel like 99% of the config work could be managed by the OS itself and a GUI to change everything else. I also feel like flakes should be the default, not this weird multiple systems thing they have. I also wish most apps would have a sandbox built in, because nix apps would then rival flatpak and, if ported to Windows, become a universal package manager. Overall good concept but not there yet.

Bypass paywall: https://web.archive.org/web/20230825195049/https://www.palmbeachpost.com/story/news/trump/2023/08/25/florida-attorneys-14th-amendment-lawsuit-aims-to-disqualify-trump/70672653007/

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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.

Ban cars is a lot like saying defund the police

It makes a whole lot of sense when you stop and examine the reasoning, but it masks the wide reaching social and infrastructure reforms needed to accomplish it with a short meme that people misinterpret.

Smh my head, Linux is too mainstream now!!! How will I be a cool hacker boy away from society if everyone else uses it!!!

Cool meme but Reuters doesn’t own AP and Rothschild doesn’t own Reuters. It is quite ironic to be pushing against the very real problem of media disinfo/government propaganda trickled down through AP/Reuters, while at the same time spreading misinformation.

This isn’t an actual problem. Can you train on post-ChatGPT internet text? No, but you can train on the pre-ChatGPT common crawls, the millions of conversations people have with the models and on audio, video and images. As we improve training techniques and model architectures, we will need even less of this data to train even more performant models.