Definitely has been true for my work. LLMs have absolutely have been useful, I even forked an IDE (Zed) to add my own custom copilot to leverage a deeper integration for my work.
But even if we consider AI beyond just NLP, there's been so much ML you can apply to other more banal day to day tasks. In my org's case, one of the big ones was anomaly detection and fault localization in aggregate network telemetry data. Worked far better than conventional statistical modeling.
I usually assume there is a caricature of "AI Tools" that all of the detractors are working backwards from that are often nothing more than a canard for the folks that are actually using AI tooling successfully in their work.
We never have any proof or source for that, and when we have one (like the Devin thing) it’s always a ridiculous project in JS with a hundred lines of code that I could write in one day.
Give me some refactoring in a C++ code base with 100k lines of code, and we’ll be able to talk.
Anything with using tools which you are not an expert with. If you know how to do things and only use one specific language or framework -- there is nothing to use AI for.
This whole area is so drenched in bullshit, it's no wonder that the generation of BS and fluff is still the most productive use. Just nothing where reliable facts matter. I do believe that machines can vomit crap 10x as fast as humans.
I had to sign a 140 page contract of foreign language legalese. Mostly boiler plate, but I had specific questions about it.
Asking questions to an AI to get the specific page answering it meant I could do the job in 2 hours. Without an AI, it would have taken me 2 days.
For programming, it's very good at creating boilerplate, tests, docs, generic API endpoints, script argument parsing, script one-liners, etc. Basically anything for which, me, as a human, don't have much added value.
It's much faster to generate imperfect things with AI and fix them that to write them myself when there is a lot of volume.
It's also pretty good at fixing typos, translating, giving word definition, and so on. Meaning if you are already in the chat, no need to switch to a dedicated tool.
I don't personally get 10x on average (although on specific well suited task I can) but I can get a good X3 on a regular basis.
spmurrayzzz|1 year ago
But even if we consider AI beyond just NLP, there's been so much ML you can apply to other more banal day to day tasks. In my org's case, one of the big ones was anomaly detection and fault localization in aggregate network telemetry data. Worked far better than conventional statistical modeling.
I usually assume there is a caricature of "AI Tools" that all of the detractors are working backwards from that are often nothing more than a canard for the folks that are actually using AI tooling successfully in their work.
LunaSea|1 year ago
JTyQZSnP3cQGa8B|1 year ago
Give me some refactoring in a C++ code base with 100k lines of code, and we’ll be able to talk.
apples_oranges|1 year ago
kemiller2002|1 year ago
Muromec|1 year ago
j4nek|1 year ago
drewcoo|1 year ago
ainewsinterest|1 year ago
tempodox|1 year ago
ramon156|1 year ago
BiteCode_dev|1 year ago
I had to sign a 140 page contract of foreign language legalese. Mostly boiler plate, but I had specific questions about it.
Asking questions to an AI to get the specific page answering it meant I could do the job in 2 hours. Without an AI, it would have taken me 2 days.
For programming, it's very good at creating boilerplate, tests, docs, generic API endpoints, script argument parsing, script one-liners, etc. Basically anything for which, me, as a human, don't have much added value.
It's much faster to generate imperfect things with AI and fix them that to write them myself when there is a lot of volume.
It's also pretty good at fixing typos, translating, giving word definition, and so on. Meaning if you are already in the chat, no need to switch to a dedicated tool.
I don't personally get 10x on average (although on specific well suited task I can) but I can get a good X3 on a regular basis.