TehPers

No idea who told you this, but MS employees use Teams exclusively.

As for it being terrible, it’s unfortunately hard to find a competitor that does better with the same feature set (video/screen sharing/text channels/sso/tenants/etc). Many get close (like Slack) but none have the whole package.

Since the bottom of an article is usually the least visible, I’ll paste this here to make it more visible:

“The Copilot Discord channel has recently been targeted by spammers attempting to disrupt and overwhelm the space with harmful content not related to Copilot. Initially, this spam consisted of walls of text, so we added temporary filters for select terms to slow this activity. We have since made the decision to temporarily lock down the server while we work to implement stronger safeguards to protect users from this harmful spam and help ensure the server remains a safe, usable space for the community,” a Microsoft spokesperson told Windows Latest.

Microsoft added that blocking terms such as “Microslop,” along with other phrases in the spam campaign, was not intended as a permanent policy but a short-term mitigation while the company manages to put additional protections in place.

Whether it’s true or not that the policy was temporary, I guess we’ll see.

In some cases, it appears to be the opposite: CEOs want to do mass layoffs, so they blame AI rather than taking accountability themselves. The Amazon layoffs reek of this.

had people understood from the start the limitations of it, investment would’ve been more modest and cautious

People did understand from the start. Those who do the investing just didn’t listen, or they had a different motive. These days it’s impossible to tell which.

And by “people” I’m not referring to random people, but those who have been closer than most to the development of these models. There has been an unbelievable amount of research done on everything from the effectiveness of specific models in niche fields to the ability to use an LLM as the backend for a production service. Again, no amount of negative feedback going up the chain has made a difference in the direction, so that only leaves a few explanations on why the investment continues to be so high.

Could also do this:

#[expect(lint, reason = "TODO: #issue")]

Edit: to clarify, #issue is an issue number that points to a related issue or task. Could also just explain it inline, but if you have a task tracker, better to make a task instead.

More complicated Tor, but super cool. It uses garlic routing rather than onion routing to further anonymize packets.

It’s worth reading into what it is (and especially those two terms) to get a better understanding.

Not exactly. Thinking models just inflate the context window to point the model closer to your target. GANs have two models which compete against each other, both training each other, with the goal of one (or both) of those models being improved over time.

This is unironically what I’ve seen people try to do, except they assume the second AI is correct.

Unrelated, but this is how GANs work to some extent. GANs train during the back-and-forth though, while LLMs do not.

Which outputs are accurate, and which ones are inaccurate? How could you tell? What steps did you take to verify accuracy? Was verifying it a manual process?

Shh you’ll pop the bubble if you start talking sensibly. It’s not an ASIC—it’s a specialized piece of hardware optimized to execute a model with unparalleled performance. Now buy my entire stock of them and all the supply for the next two years please.

(Figuring out the compose combination for an emdash took longer than I’d like to admit lol)

Do we know it doesn’t?

Terminator might be a little more popular.

It seems the only way to win is not to play.

For what it’s worth, open source dev can also work. If you can commit some time to a project you care deeply about and make regular contributions, that’s another form of experience, and I see no reason you couldn’t add that as a line to your resume alongside any other work experience.

This has always been an issue. From my experience, the best way to get in was through internships, co-ops, and other kinds of programs. Those tend to have lower requirements and count as experience.

Of course, today, things are a lot different. It’s a lot more competitive, and people don’t care anymore about actual software dev skills, just who can churn out SLOC the fastest.

*Several states

Washington, for example, has a similar bill proposed.

To put some perspective into what our code looks like, there are very few tests (which may or may not pass), no formatter or linter for most of the code, no pipelines to block PRs, no gates whatsoever on PRs, and the code is somewhat typed sometimes (the Python, anyway). Our infrastructure was created ad-hoc, it’s not reproducible, there’s only one environment shared between dev and prod, etc.

I’ve been in multiple meetings with coworkers and my manager talking about how it is embarassing that this is what we’re shipping. For context, I haven’t been on this project for very long, but multiple projects we’re working on are like this.

Two years ago, this would have been unacceptable. Our team has worked on and shipped products used by millions of people. Today the management is just chasing the hype, and we can barely get one customer to stay with us.

The issue lies with the priorities from the top down. They want new stuff. They don’t care if it works, how maintainable it is, or even what the cost is. All they care about is “AI this” and “look at our velocity” and so on. Nobody cares if they’re shipping something that works, or even shipping the right thing.

Colleagues, and the issue is top-down. I’ve raised it as an issue already. My manager can’t do anything about it.

Because if I spent my whole day reviewing AI-generated PRs and walking through the codebase with them only for the next PR to be AI-generated unreviewed shit again, I’d never get my job done.

I’d love to help people learn, but nobody will use anything they learn because they’re just going to ask an LLM to do their task for them anyway.

This is a people problem, and primarily at a high level. The incentive is to churn out slop rather than do things right, so that’s what people do.

This is what happens to us. People put out a high volume of AI-generated PRs, nobody has time to review them, and the code becomes an amalgamation of mixed paradigms, dependency spaghetti, and partially tested (and horribly tested) code.

Also, the people putting out the AI-generated PRs are the same people rubber stamping the other PRs, which means PRs merge quickly, but nobody actually does a review.

The code is a mess.

Why would you comment on an article you haven’t read and have no interest in?