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.

Only thing really missing is Wallet and NFC support. Other than that I think Graphene and Lineage OS cover it all

Hello, kids! Pirates are very bad! Never use qBittorent to download copyrighted material, and certainly do NOT connect it to a VPN to avoid getting caught. Additionally, you should also NEVER download illegal material via an https connection because it is fully encrypted and you won’t get caught!

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This is another reminder that the anomalous magnetic moment of the muon was recalculated by two different groups using higher precision lattice QCD techniques and wasn’t found to be significantly different from the Brookhaven/Fermilab “discrepancy”. More work needs to be done to check for errors in the original and newer calculations, but it seems quite likely to me that this will ultimately confirm the standard model exactly as we know it and not provide any new insight or the existence of another force particle.

My hunch is that unknown particles like dark matter rely on a relatively simple extension of the standard model (e.g. supersymmetry, axioms, etc.) and the new physics out there that combines gravity and QM is something completely different from what we are currently working on and can’t be observed with current colliders or any other experiments on Earth.

So probably we will continue finding nothing interesting for quite some time until we can get a large ML model crunching every single possible model to check for fit on the data, and hopefully derive some better insight from there.

Though I’m not an expert and I’m talking out of my ass so take this all with a grain of salt.

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.

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

Sam Altman: We are moving our headquarters to Japan

For the love of God please stop posting the same story about AI model collapse. This paper has been out since May, been discussed multiple times, and the scenario it presents is highly unrealistic.

Training on the whole internet is known to produce shit model output, requiring humans to produce their own high quality datasets to feed to these models to yield high quality results. That is why we have techniques like fine-tuning, LoRAs and RLHF as well as countless datasets to feed to models.

Yes, if a model for some reason was trained on the internet for several iterations, it would collapse and produce garbage. But the current frontier approach for datasets is for LLMs (e.g. GPT4) to produce high quality datasets and for new LLMs to train on that. This has been shown to work with Phi-1 (really good at writing Python code, trained on high quality textbook level content and GPT3.5) and Orca/OpenOrca (GPT-3.5 level model trained on millions of examples from GPT4 and GPT-3.5). Additionally, GPT4 has itself likely been trained on synthetic data and future iterations will train on more and more.

Notably, by selecting a narrow range of outputs, instead of the whole range, we are able to avoid model collapse and in fact produce even better outputs.

I’ve never used Manjaro but the perception I get from it is that it is a noob friendly distro with good GUI and config (good) but then catastrophically fails when monkeying around with updates and the AUR. This is a pain for technical users and a back-to-Windows experience for the people it’s targeted towards. Overall, significantly worse than EndeavorOS or plain 'ol vanilla Arch Linux.

We have no moat and neither does OpenAI is the leaked document you’re talking about

It’s a pretty interesting read. Time will tell if it’s right, but given the speed of advancements that can be stacked on top of each other that I’m seeing in the open source community, I think it could be right. If open source figured out scalable distributed training I think it’s Joever for AI companies.

I don’t know what type of chatbots these companies are using, but I’ve literally never had a good experience with them and it doesn’t make sense considering how advanced even something like OpenOrca 13B is (GPT-3.5 level) which can run on a single graphics card in some company server room. Most of the ones I’ve talked to are from some random AI startup that have cookie cutter preprogrammed text responses that feel less like LLMs and more like a flow chart and a rudimentary classifier to select an appropriate response. We have LLMs that can do the more complex human tasks of figuring out problems and suggesting solutions and that can query a company database to respond correctly, but we don’t use them.

This makes sense for any other company but OpenAI is still technically a non profit in control of the OpenAI corporation, the part that is actually a business and can raise capital. Considering Altman claims literal trillions in wealth would be generated by future GPT versions, I don’t think OpenAI the non profit would ever sell the company part for a measly few billions.

Lmao Twitter is not that hard to create. Literally look at the Mastodon code base and “transform” it and you’re already most of the way there.

I used to be on GrapheneOS, but the drama with the developer plus mainly not being able to put my university ID on the wallet, forced me back on stock Android.

Besides Android, I use Google Play Store, YouTube, and Maps. For YouTube I’ve technically degoogled, using Invidious and NewPipe, but that’s obviously still using Google services.

I really wish that digital payment didn’t rely on two proprietary services (Google Wallet and Apple Wallet). It would be so much easier for phone companies to ship privacy friendly versions of Android if there was a FOSS alternative directly integrated into AOSP. I also wish apps didn’t have to use Google service framework just to function, it seems stupid af. I don’t think this will ever improve, so I’ll probably end up on a true Linux phone whenever those catch up (2030 YEAR OF THE LINUX PHONE???)

We also need open collaboration on mapping. There is the OpenStreetMaps and Overture maps from Linux foundation, but those aren’t really there yet unfortunately.

The natural next place for people to go to once they can’t block ads on YouTube’s website is to go to services that exploit the API to serve free content (NewPipe, Invidious, youtube-dl, etc.). If that happens at a large scale, YouTube might shut off its API just like Reddit did and we’ll end up in scenario where creators are forced to move to Peertube, and, given how costly hosting is for video streaming, it could be much worse than Reddit->Lemmy+KBin or Twitter->Mastodon. Then again, YouTube has survived enshittiffication for a long time, so we’ll have to wait and see.

FediSearch I guess is similar to your idea, though I think the goal would be to make a new and open search index specifically containing fediverse websites instead of just using Google. I also feel like the formatting should be more like Lemmy, with the particular post title and short description showing instead of the generic search UI.

The idea of a fediverse search is really cool though. If things like news and academic papers ever got their own fediverse-connected service, I could see a FediSearch being a great alternative to the AI sludge of Google.