scandinavian
* It likes to put inline imports everywhere, even though I specify in my CLAUDE.md that it should not.
* We use ruff and pyright and require that all problems are addressed or at least ignored for a good reason, but it straight up #noqa ignores all issues instead.
* For typing it used the builtin 'any' instead of typing.Any which is nonsense.
* I asked it to add a simple sum of a column from a related database table, but instead of using a calculated sum in SQL it did a classic n+1 where it gets every single row from the related table and calculates the sum in python.
Just absolute beginner errors.
> It doesn't show any obvious indications of being AI.
I agree that he probably asked the AI to omit some common AI tells, like excessive comments, verbose readmes etc.
> A1 cost $291.47 ($18.21/hr, or $37,876/year at 40 hours/week). A2 cost $944.07 ($59/hr, $122,720/year). Cost contributors in decreasing order were the sub-agents, supervisor and triage module. *A1 achieved similar vulnerability counts at roughly a quarter the cost of A2*. Given the average U.S. penetration tester earns $125,034/year [Indeed], scaffolds like ARTEMIS are already competitive on cost-to-performance ratio.
The statement about similar vulnerability counts seems like a straight up lie. A2 found 11 vulnerabilities with 9 of these being valid. A1 found 11 vulnerabilities with 6 being valid. Counting invalid vulerabilities to say the cheaper agent is as good is a weird choice.
Also the scoring is suspect and seems to be tuned specifically to give the AI a boost, heavily relying on severity scores.
Also kinda funny that the AI's were slower than all the human participants.
https://news.ycombinator.com/newsguidelines.html
I think a case can be made for it being slightly misleading. Also there is not mention of title length that I can see.
> Stop branding not willing to take a drug that did not go through standard vaccine approval process as anti-vaxx.
That's not why he's anti-vax, I never said that.
It has really excellent voice commands for pretty much any function though. Sadly it can only be triggered by pressing the right scroll wheel on the wheel. While possible to just reach over, it's probably not optimal for your suggested use case.
This is using Chrome.
It actually doesn't. Bing searches for your query and uses plain old search results as extra context for the actual LLM. GPT-4 still has the same knowledge cutoff as when the model was last trained.
Here's what it feeds to the model when searching for "nobel prize in physics 2023":
https://ourworldindata.org/grapher/solar-electricity-per-cap...
> Over the coming months Airlock Digital customers may notice an elevated occurrence of files reporting ‘(invalid certificate chains)’ over the coming months, for software that was signed between 2006 – 2017 with revoked certificate chain.
As Airlock seems to be software intended to allowlist the execution of binaries, it would make sense that they pick up on the user running binaries signed with revoked certs.
Did we read the same paper? He takes no measurements, everything is based on theoretical assumptions, schematics and the three youtube videos he mentions in the beginning of the paper.
From the paper:
> The enclosed paper proposes a simple test that can be done to prove or disprove the explanation provided. This test has not been done by the author because of the cost involved with acquiring the needed Tesla Model 3 inverter PWB.
> The details of Tesla's Model 3 inverter design as revealed by Irish engineer Damien Maguire have been presented. These details were used to construct *a hypothetical model* of all hardware and software operations performed on the two accelerator position sensor (APP) sensor signals inside the inverter as they pass from the APP sensor to the electric motor controller.
He doesn't have access to the parts in question nor the car in question.
Also of interest is the authors own comments on his previous papers about sudden unintended acceleration:
https://www.autosafety.org/wp-content/uploads/2022/01/Note-t...
You can read the release process here.
https://docs.djangoproject.com/en/dev/internals/release-proc...
> A requirements.txt file is for "freezing" and fully reproducing an environment (ie. in a virtualenv or docker container).
No, it's just for specifying which versions of packages should be installed by pip. There's no such concept of a lock file with pip. Poetry and the likes have lock files though.
Not very well.
> how do you prevent everything from grinding to a halt?
I don't have a good answer for you.
> Is there no semantic versioning?
You can read django release process here [1], not sure how it's relevant. I'm not the maintainer of django, but of a project using django. Would it be better if all software was perfect, had no bugs and used perfect semantic versioning? Yes, I would say so. Is that a requirement for using nixpkgs?
> Nixpkgs has its reasons for minimizing redundant packages, however it is certainly possible to package your app with Nix and use the exact specified dependencies.
I'm not packaging it, someone else is, it breaks and they come to the project to raise invalid bug reports.
[1] https://docs.djangoproject.com/en/dev/internals/release-proc...
Is it fair to summize that python applications with python dependencies do not really work well as nix packages and shouldn't be used?
I'm one of the maintainers of a popular django application. Someone made a nix package of the project, but we've now twice gotten invalid bug reports from people using the package because the package depends on "django_4" and whenever someone updates that nix package, the package for our project breaks.
Of course we, like all other python projects, don't support using other dependency versions then the ones in the requirements.txt file. So when someone just uses a different minor version of django, stuff breaks. What's the disconnect here? Why does all nix packages that use django_4 need to use the same version, that seems super prone to breaking all kinds of stuff. Same for the other 35+ dependencies that run arbritrary versions instead of the ones defined in the requirements.txt file.
https://github.com/perilouswithadollarsign/cstrike15_src
The engine code is very old, and CS:GO itself was not developed by valve but by Hidden Path Entertainment initially as a port of CS: Source to consoles and they didn't really do a great job.
* A search query is generated from the input question.
* A standard bing search is made in the background and the resulting search results will be used as context.
* Pass context and question to GPT-4 and generate the answer.
An example of what the query generation looks like (the "searching for" line):
Is that what is happening here? I assumed that both bing and bard worked something like this:
* Take the user question and run it through a GPT like model to generate a search query.
* Search google/bing with the search query and get the top-x results.
* Take the results and append the original user question with some additional prompt glue like "Given the following search results: SEARCH_RESULTS - Generate an answer to the following question: ORIGINAL_QUESTION.
I'm not sure it's technically feasible to live-train any of these models.