This company predicts software development is a dead occupation yet ships a mobile chat UI that appears to be perpetually full of bugs, and has had a number of high profile incidents.
"This company predicts software development is a dead occupation"
Citation needed?
Closest I've seen to that was Dario saying AI would write 90% of the code, but that's very different from declaring the death of software development as an occupation.
The clear disdain he has for the profession is evident in any interview he gives. Him saying 90% of the code was not a signal to us, but it was directed to his fellow execs, that they can soon get rid of 90% of the engineers and some other related professions.
I think it's pretty clear that Anthrophic was the main AI lab pushing code automation right from the start. Their blog posts, everything just targeted code generation. Even their headings for new models in articules would be "code". My view if they weren't around, even if it would of happened eventually, code would of been solved with cadence to other use cases (i.e. gradually as per general demand).
AI Engineers aren't actually SWE's per se; they use code but they see it as tedious non-main work IMO. They are happy to automate their compliment and raise in status vs SWE's who typically before all of this had more employment opportunities and more practical ways to show value.
I dislike the idea of coupling my workflow to saas platforms like github or code rabbit. The fact that you still have to create local tools is a selling point for just doing it all “locally”.
I’ve been doing game development and it starts to hallucinate more rapidly when it doesn’t understand things like the direction it placing things or which way the camera is oriented
Gemini models are a little bit better about spatial reasoning, but we’re still not there yet because these models were not designed to do spatial reasoning they were designed to process text
In my development, I also use the ascii matrix technique.
Spatial awareness was also a huge limitation to Claude playing pokemon.
It really seems to me that the first AI company getting to implement "spatial awareness" vector tokens and integrating them neatly with the other conventional text, image and sound tokens will be reaping huge rewards.
Some are already partnering with robot companies, it's only a matter of time before one of those gets there.
I disagree. With opus I'll screenshot an app and draw all over it like a child with me paint and paste it into the chat - it seems to reasonably understand what I'm asking with my chicken scratch and dimensions.
As far as 3d I don't have experience however it could be quite awful at that
Yeah at least for 2D, Opus 4.5 seems decent. It can struggle with finer details, so sometimes I’ll grab a highlighter tool in Photoshop and mark the points of interest.
I wonder if they could integrate a secondary "world model" trained/fine-tuned on Rollercoaster Tycoon to just do the layout reasoning, and have the main agent offload tasks to it.
I expect that adding instructions that attempt to undo training produces worse results than not including the overbroad generalization in the training in the first place. I think the author isn’t making a complaint they’re documenting a tradeoff.
I have been using an open source program “handy”, it is a cross platform rust tauri app that does speech recognition and handles inputting text into programs. It works by piggybacking off the OS’s text input or copy and paste features.
You could fork this, and shell out to an LLM before finally pasting the response.
The definition of 'vibe code' is somewhat nebulous at the moment. For many it means "only look at the end product (website) and use prompts to fix it" but for others it means "mostly don't hand-code anything, but check the diffs".
> If you’re not reading your output, then why does skill level even matter?
Few thoughts here.
Experience helps you "check" faster that what you asked for is actually what was delivered. You "know" what to check for. You know what a happy path is, and where it might fail. You're more likely to test outside the happy path. You've seen dozens of failure modes already, you know where to look for.
Experience also allows you to better define stuff. If you see that the output is mangled, you can make an educated guess that it's from css. And you can tell the model to check the css integration.
Experience gives you faster/better error parsing. You've seen thousands of them already. You probably know what the error means. You can c/p the error but you can also "guide" the model with something like "check that x is done before y". And so on.
Last, but not least, the "experience" in actually using the tools gives you a better understanding of their capabilities and failure modes. You learn where you can let it vibe away, or where you need to specify more stuff. You get a feeling for what it did from a quick glance. You learn when to prompt more and where to go with generic stuff like "fix this".
Now roast the horrendous ugly colors on this horrendous ugly website that is frying my eyeballs. Did you prompt your LLM to create the biggest pile of garbage possible? Or is that just how you talk about other peoples work?
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