After just spending 15 minutes trying to get something useful accomplished, anything useful at all, with latest beta Apple Intelligence with a M1 iPad Pro (16G RAM), this article appealed to me!<p>I have been running the 32B parameters qwen2.5-coder model on my 32G M2 Mac and and it is a huge help with coding.<p>The llama3.3-vision model does a great job processing screen shots. Small models like smollm2:latest can process a lot of text locally, very fast.<p>Open source front ends like Open WebUI are improving rapidly.<p>All the tools are lining up for do it yourself local AI.<p>The only commercial vendor right now that I think is doing a fairly good job at an integrated AI workflow is Google. Last month I had all my email directed to my gmail account, and the Gemini Advanced web app did a really good job integrating email, calendar, and google docs. Job well done. That said, I am back to using ProtonMail and trying to build local AIs for my workflows.<p>I am writing a book on the topic of local, personal, and private AIs.
I don't want to be an "AI engineer" in the way the article means. There's nothing about that sort of job that I find interesting or exciting.<p>I hope there will still be room for devs in the future.
Is that really AI engineering or Software engineering with AI?<p>If a model goes sideways how do you fix that? Could you find and fix flaws in the base model?
I feel like I see this comment fairly often these days, but nonetheless, perhaps we need to keep making it - the AI generated image there is so poor, and so off-putting. Does anyone like them? I am turned off whenever I see someone has used one on a post, with very few exceptions.<p>Is it just me? Why are people using them? I feel like objectively they look like fake garbage, but obviously that must be my subjective biases, because people keep using them.
An AI engineer with some experience today can easily pull down 700K-1M TC a year at a bigtech. They must be unaware that the "barriers are coming down fast". In reality it's a full time job to just _keep up with research_. And another full time job to try and do something meaningful with it. So yeah, you can all be AI engineers, but don't expect an easy ride.
Large Language Models (LLMs) don’t fully grasp logic or mathematics, do they? They generate lines of code that appear to fit together well, which is effective for simple scripts. However, when it comes to larger or more complex languages or projects, they (in my experience) often fall short.
I still don't see how AI replaces the understanding of what a server is, what DNS is, what HTTP is, or what...<p>I could go on and on.<p>Copy paste is great until you literally dont know where you are copy and pasting
I mean, sure, anyone can cobble together Ollama and a wrapper API and an adjusted system prompt, or go serious with Bumblebee on the BEAM.<p>But that's akin to web devs of old that stitched up some cruft in Perl or PHP and got their databases wiped by someone entering a SQL username. Yes, it kind of works under ideal conditions, but can you fix it when it breaks? Can you hedge against all or most relevant risks?<p>Probably not. Don't put it your toys into production, and don't tell other people you're a professional at it until you know how to fix and hedge and can be transparent about it with the people giving you money.