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brudgers 4 days ago [-]
[random comment on the internet]
Some content on the subject of AI is deeply technical, just as is some content on the subject of blockchain, lisp, retro-computing, etc.
But most of what is written about most things (including C, reverse engineering, systems programming, etc.) is not deeply technical.
One problem unique to AI is that “AI” doesn’t mean anything specific…I mean even your question doesn’t distinguish between articles about specific AI technologies, articles generated using LLM’s, “AI” as a marketing feature, AI as an industry, AI as an ideology etc.
dieselgate 3 days ago [-]
Textbooks, white papers and research publications if I understand what you mean by "deep technical". I like lectures and stuff too but find it difficult to refer back to later and search around.
bediger4000 4 days ago [-]
At least part of the problem is that deep technical content doesn't get upvotes. The next time you see a link to such, note the vote count. It will be small. Note the vote count on some "AI" boosting link. It will be large. Is this Anthropic, Google and OpenAI bots for is it genuine interest?
iefbr14 4 days ago [-]
There is probably still a lot out there but with the current state of the ('free') search engines you won't find much. I am painfully reminded of that every time I have to look for a datasheet that is not in my own archive yet.
Be specific! You must know what you want to read at a time, always prefer going with fundamentals, textbooks and get keywords. Use those keywords to find out the deeper level content.
dnnddidiej 3 days ago [-]
For Go in particular: the docs. Gophercon videos.
In general good conference then Youtube. Even if old e.g. strangeloop. There is Fosdem etc.
bohdanstefaniuk 3 days ago [-]
In most cases documentation is enough for me when I need some help with my current day to day tasks.
If I want do dig deeper - textbooks, white papers are still a good source.
Some content on the subject of AI is deeply technical, just as is some content on the subject of blockchain, lisp, retro-computing, etc.
But most of what is written about most things (including C, reverse engineering, systems programming, etc.) is not deeply technical.
One problem unique to AI is that “AI” doesn’t mean anything specific…I mean even your question doesn’t distinguish between articles about specific AI technologies, articles generated using LLM’s, “AI” as a marketing feature, AI as an industry, AI as an ideology etc.
In general good conference then Youtube. Even if old e.g. strangeloop. There is Fosdem etc.
If I want do dig deeper - textbooks, white papers are still a good source.