Great post, thank you!
8 karma · joined July 17, 2023
Great post, thank you!
We need to further emphasize the importance of responsibility when using LLM tools to produce output for others. It's great to use them for refactoring and bug discovery, but keep in mind that it's your responsibility to analyze it and iterate on it with AI. It makes your code better and develops technical expertise.
The "Hey, analyze that codebase, find all dangerous CVEs, and write a README for the PoCs, so I can post it online for others to analyze, and if I'm lucky, I'll get a paycheck or a title to add to my resumé" approach might work in 0.1% of cases, but it will generate a ton of slop for the community to drown in.
I also think GPTZero and other AI detectors have far more false positives than correct guesses. I tried it on several texts & messages I wrote before 2019, and it flagged them as 80% AI-generated.
In that case, it's reasonable to assume that AI also generated the README text for each discovered CVE. In other cases, however, we should be more cautious.
You could try ScreenZen or a similar app that help control nd reduce the time you spend on certain apps.
Nowadays, you can such apps in a few prompts and some minor tweaks :)
[0] https://geohot.github.io/blog/jekyll/update/2025/02/24/money...
it originally referred to the idea that neural signals might form an intrinsic "language" representing aspects of the world, though these signals gain meaning only through interpretation in context.
in artificial intelligence, the term now has a more concrete role, referring to the deep communication protocols used by multiagent systems.
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