Comment by n_ary

There are indeed productivity gains, but those are more scattered to quantify.

Here are some significant productivity gains I get from Mistral/Phind/ChatGPT/office-internal-llm daily.

- throw a messy shell script and ask it to refactor it(works 80% of the time)

- put a sample xml/json/yaml and ask it to generate the class/struct (code generation)

- ask questions and it gives immediate response with example more well suited to my need (previously took time to go into SO/Reddit/SE etc and scroll through several posts, docs or even waste time reading blogspams )

- ask questions about specific topic and get immediate response and citations(this is inhouse trained model) instead of fighting with broken search or ocean of messy documents in Confluence/Notion/Gitlab Pages and what not

- rubber duck when brainstorming a problem(it can sometimes lead to interesting outcomes)

- prepare a bash script to do something and then I simply modify/correct/refine it to fit my needs

- questions about trivial stuff

- generate boiler plates

- generate a throw away project to try something fast

- convert from one language to another(need to work with different teams using different languages such as TS/Java/C++/Scala/Python/Shell/Rust/Erlang etc)

- write a polite email(or response to) which I can copy paste and send when I am too occupied with something else

- documentation of specific feature of something which would take a lot of digging in the original docs

- generate a pure self-contained html/css prototype to send to our UI/UX team to give them an idea of particular concept

- summarize large block of text into bullet forms(useful for presentations)

- get summaries of popular books(because chatgpt has indeed trained on a lot of them somehow!)

- translate a text to another language(works well when it does but still needs some corrections)

Most of these activities save me a lot of time which would previously need some big time investments.