To be fair, the bug report was utterly useless too.
this post was submitted on 24 Nov 2023
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True, when i respond with the exact problem it usually gets fixed, interestingly even explained why it failed.
Great for learning
The only problem is that it'll ALSO agree if you suggest the wrong problem.
"Hey, shouldn't you have to fleem the snort so it can be repurposed for later use?"
You are correct. Fleeming the snort is necessary for repurposing for later use. Here is the updated code:
Models are geared towards seeking the best human response for answers, not necessarily the answers themselves. Its first answer is based on probability of autocompleting from a huge sample of data, and in versions that have a memory adjusts later responses to how well the human is accepting the answers. There is no actual processing of the answers, although that may be in the latest variations being worked on where there are components that cycle through hundreds of attempts of generations of a problem to try to verify and pick the best answers. Basically rather than spit out the first autocomplete answers, it has subprocessing to actually weed out the junk and narrow into a hopefully good result. Still not AGI, but it's more useful than the first LLMs.
That's not been my experience. It'll tend to be agreeable when I suggest architecture changes, or if I insist on some particular suboptimal design element, but if I tell it "this bit here isn't working" when it clearly isn't the real problem I've had it disagree with me and tell me what it thinks the bug is really caused by.
It was trying to is, then it isn'ted. Help?
Literally why docker was invented
Yeah, it "solved" the "it works on my machine" by bundling the machine with the code.
Man, I really was interested in that topic, but that guy really can't do talks.
What about this? https://youtu.be/5XY3K8DH55M
Also I created this repo to create a reproducible sec environment for myself. I added other languages, but personally work mostly with python. It is basically resonating for handling all the boiler plate:
https://github.com/takeda/nix-cde
For packaging in docker I started to use nix2container project as it gives me a greater control over layers. So for example when I package my phyton app I typically use 3 layers:
- python and it's dependencies
- my application dependencies
- my application, which is very tiny compared to other two, so there is great reuse of the layers
The algorithm mentioned in the video also helps a lot with reuse, but the above is more optimized by frequency of how things typically change.
BTW: today I discovered this https://github.com/astro/microvm.nix I haven't play with it yet, but in theory it would let me generate a microvm image (in similar fashion to generate a docker container) which would let me to run my app natively as a tiny VM on EC2 for example, and use only minimum necessary of a typical OS to run it.
Docker has been a savior.
Now we just need to run docker inside the browser
Ah-ah! Now that’s progress!
Every time I hear this from one of my devs under me I get a little more angry. Such a meaningless statement, what are you gonna do, hand your pc to the fucking customer?
It's not actually meaningless. It means "I did test this and it did work under certain conditions." So maybe if you can determine what conditions are different on the customer's machine that'll give you a clue as to what happened.
The most obscure bug that I ever created ended up being something that would work just fine on any machine that had at any point had Visual Studio 2013 installed on it, even if it had since had it uninstalled (it left behind the library that my code change had introduced a hidden dependency on). It would only fail on a machine that had never had Visual Studio 2013 installed. This was quite a few years back so the computers we had throughout the company mostly had had 2013 installed at some point, only brand new ones that hadn't been used for much would crash when it happened to touch my code. That was a fun one to figure out and the list of "works on this machine" vs. "doesn't work on that machine" was useful.
...yes? I thought we made that clear with containerization
"my devs under me"
Lols.
Then we'll ship the AI.
...what do you mean, all the ICBM silo doors are opening?
ChatGPT is far too long, let's call it WOPR. The most capable tic tac toe machine.
https://en.wikipedia.org/wiki/WarGames
But it doesn't even compile!
"You literally just wrote 'kill all humans' and put it in curly brackets."
You know what, this is on us. Chat GPT is just a prediction engine that tries to say what words it thinks follow a preceding prompt, and it’s looked at millions of examples (written by us) and it’s seen hapless clients and users complain about bugs and be told: “user error, works fine” so often chatGPT just thinks it’s just the culturally accepted polite response to a bug report, in the same way as responding to “thank you” with “you’re welcome”. This is a dark mirror on our profession.
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