Nah man, no you don’t. There are very few absolutes in the world and that certainly isn’t one of them.

It’s mostly from people using their personal accounts to run LLM services that serve a larger team or organization. At least at Microsoft, it’s still impressively hard to get access to an LLM for service usage with high enough rate limits to be useful, making running services on dev boxes much more appealing (despite the countless drawbacks that few people seem to care about around security, compliance, reliability, etc).

It’s interesting that models seem, to a distant outsider of biology and drug discovery like me, to be good at coming to new conclusions from existing data. I feel like in coding, it’s the opposite - I have to drag the models kicking and screaming towards anything resembling a novel or nuanced approach to some problems. If anything, this behavior in coding is why we say senior+ engineers will continue to be high value employees, because we can steer the models away from boilerplate and overly generic solutions towards ones that fit aspects of the domain we understand more intrinsically.

This could easily just be how it looks from the outside of biology, but it does seem to produce more novel conclusions in biology than it does in coding and art. Curious if others have counter examples…

The dotnet team provides some agent plugins which solve some of these issues. I expect unions in dotnet 11 will need some skill updates too.

I wonder if any of those work alongside Github Copilot (the autocomplete not actual agent).

Because at this point I would do anything if I could get Github Copilot to understand the new language features and stop trying to constantly revert code.

The one specific thing I'm constantly having to deny is Copilot seeing this:

    List items = [];

into this:

    List items = new List();

The change with this justification absolutely exists and has existed for a long time, but this justification only works for small changes (in the past and still today). Don’t come to me with a huge refactor or new feature and this explanation, which is happening more and more recently (it’s an easy strawman to make and hard to prove, but I certainly see it in practice).

Some of us don’t use an adblocker because we are aware that ads are how most websites get paid for on the internet. I pay for sites I care to support, but for something like LinkedIn, I’d rather have their monetization scheme push me away from any usage than block the ads and make the site slightly more palatable.

No, ads mean the customer is the advertiser, not me, the user, and align the business' interest with the advertiser's, against my own.

I'd rather the site asked me to pay directly, or not at all. I will not see any ads I can avoid.

I think this is a pretty unfair takeaway when AI generated text is often so wordy, flowery, and full of jargon and abstract nouns. People are never going to read East of Eden or academic dissertations as quickly as direct, simple language either

It's hard to talk about it in a very general way without being wrong a significant percentage of the time.

I suspect we're both right, just on different occasions.

Because this is a thread about other people's usage of AI, so how I/you personally use it is irrelevant because we still have to deal with everyone else pasting AI, in which case it is generally long and wordy.