This is quite insane to me. If I compare the output of LLMs for python vs statically typed languages it's really not a good choice to go the python route. It consistently produce relatively garbage code along actually good code. My experience has that the better static typing you have the better the code becomes.
LLMs have made me move away more from python rather than into it. I'm very surprised by this experiences of the author. The article is all over the place as well. Going basically all in on Python because it is apparently better than Haskell for LLM use and than agreeing with someone that says Rust is the best.
Do you those 48 days are the total number of working hours these MEPs have? Of course not. Something always needs to give when going on vacation. And usually missing a a plenary session is not weird or the end of the world. The problem here is that in this case the rules were abused.
sleeping helps learning, but people can learn without sleep. You can teach something to a person and they can usually do it immediately in some capacity. If you prevent a person from sleeping they will have reduced capacity to remember things. But it will most certainly not be zero.
That's precisely what a regression test suite is for. There is a bug, you fix the bug, you add a regression test. So if the test suite is well maintained these real world production scars are reflected in the tests.
I don't think it's generally thought of an advantage that LLMs are stateless. In deployment it's nice, it would be much better if the model could continually learn.
I'm not claiming LLMs are not useful, they most certainly are.
Teaching modifies the learner. Prompting doesn't modify the model. It provides additional context that influences a single inference. A person who has learned something can apply it years later without being reminded. An LLM generally cannot unless the knowledge is incorporated into the model itself or provided again.
I'm not against the prompt being changed, the point I was making is that an LLM is prone to the exact same mistakes even if you change the prompt. A trivial example is the very basic character counting mistake, I just asked chatgpt:
> How many p are in strawperry?
> There are 0 “p”s in strawperry.
And I can trigger the same mistake with various words even when adjusting the prompt many times. So I cannot teach chatgpt to correctly count characters.
> If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file ...
That's all just prompting.
> How do you think models are created? They are trained on feedback and learn.
No one is post training models on a single mistake. At least I have not seen it. I also doubt it is effective. Post-training on a single failure will not meaningfully change the model. That even sidesteps the entire problem that you don't even have access to models if you use a provider like anthropic/openai
Literally the entirety of the worlds infrastructure relies on that. In the past we had (literally) had nuclear war hinging on a single person just deciding that some data point is an artifact.
The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
Engineering is most certainly about being better or worse. A key aspect of being an engineer is that you can make conscious trade offs that include time, cost and feasability among others. It's not always a good choice the make the minimal thing that ticks of all the must requirements. In all cases there are unstated requirements that any engineer worth their salty will think and ask about. If you dont the that's how you get angry customers. That's how you get shitty quality bridges and buildings or cost blowouts. That's how you get bug ridden software. That's how you get the windows start menu.
I use DP alt mode and also the HDMI port to drive two screens. I use hyprland and runs fine after configuration.
My battery certainly last a few hours. I use it as any other laptop I had before and use it daily on trains. So I cannot at all agree with your POV that it's a desktop. The battery is working without problems.
LLMs have made me move away more from python rather than into it. I'm very surprised by this experiences of the author. The article is all over the place as well. Going basically all in on Python because it is apparently better than Haskell for LLM use and than agreeing with someone that says Rust is the best.