TehPers

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?

Is this an esolang? Could be nice for code golf maybe.

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)

Can’t speak for Git, but caching responses is a common enough problem that it’s built into the standard HTTP headers.

As for building a cache, you’d want to know a few things:

• What is a cache entry? In your case, seems to be an API response.
• How long do cache entries live? Do they live for a fixed time (TTL cache)? Do you have a max number of cached entries before you evict entries to make space? How do you determine which entries to evict if so?
• What will store the cache entries? It seems like you chose Git, but I don’t see any reason you couldn’t start simple just by using the filesystem (and depending on the complexity, optionally a SQL DB).

You seem locked into using Git, and if that’s the case, you still need to consider the second point there. Do you plan to evict cache entries? Git repos can grow unbounded in size, and it doesn’t give you many options for determining what entries to keep.

Pre-LLM translation services also generally used AI, just via more traditional machine learning. The only difference is introducing a locally run LLM.

If it runs locally and is openly available, then it doesn’t make much difference to me if it’s a traditional model or a LLM.

Do we know it doesn’t?

Terminator might be a little more popular.

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

The article doesn’t say they’re considering everything OTC, and the interview shows they explicitly are not.

That being said, it would be very entertaining to see opioids, amphetamines, etc. sold OTC.

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.

Then when they need senior devs again, they can make a good offer.

*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?

And before people ask about sniffing it, the second paragraph:

People often recognize spoiled meat through a characteristic rotting odor caused by chemical compounds called biogenic amines or BAs. Food quality inspectors quantify these compounds using procedures that involve direct meat sampling and time-consuming laboratory analysis. However, once meat is sealed and distributed for commercial retail, such testing becomes impractical, making spoilage difficult to detect.

You can’t sniff it through the packaging. Even when opened, your nose isn’t accurate enough to know if something has just started to spoil, or if only a little bit of it has. And not everyone has good (or any) sense of smell.