Microsoft steeply lowered expectations on the AI Sales team, though they have denied this since they got pummelled in their quarterly and there's been a lot of news about how investors are not happy with all the circular AI investments pumping those stocks. When the bubble pops (and all signs point to that), investors will flee. You'll see consolidation, buy-outs, hell maybe even some bullshit bailouts, but ultimately it has to be a sustainable model and that means it will cost developers or they will be pummeled with ads (probably both).

A Majority of CEOs are saying their AI spend has not paid off. Those are the primary customers, not your average joe. MIT reports 95% generative AI failure rate at companies. Altman still hasn't turned a profit. There are Serious power build-out problems for new AI centers (let alone the chips needed). It's an overheated reactionary market. It's the Dot Com bubble all over again.

There will be some more spending to make sure a good chunk of CEOs "add value" (FOMO) and then a critical juncture where AI spending contracts sharply when they continue to see no returns, accelerated if the US economy goes tits up. Then the domino's fall.

Hah, they wish. It's a business, and they need a return on investment eventually. Maybe if we were in a zero interest rate world again, but even that didn't last.

Unless I misunderstood, it will eventually dry up? Investors aren't going to be willing to give money with no returns indefinitely

With Uber that was indeed the plan and it worked. The same plan was there for AI, but AI isn't doing so well on the whole overtake and starve out thing. They'll have to jump directly to hiking prices. So it's only kinda like Uber.

So far, there is serious cognitive step needed that LLM just can't do to get productive. They can output code but they don't understand what's going on. They don't grasp architecture. Large projects don't fit on their token window. Debugging something vague doesn't work. Fact checking isn't something they do well.

Can you cite some sources on the increased efficiency? Also, can you link to these lower priced, efficient (implied consumer grade) GPUs and TPUs?

TheRegister had an article, a year or 2 ago, about using AI in the opposite way: instead of creating the code, someone was using it to discover security-problems in it, & they said it was really useful for that, & most of its identified things, including some codebase which was sending private information off to some internet-server, which really are problems.

I wonder if using LLM's as editors, instead of writers, would be better-use for the things?

_ /\ _

Might make open source more trustworthy, It can't be any harder to check than closed source.

I used to use emojis in my documentation very lightly because I thought they were a good way to provide visual cues. But now with all the people vibe coding their own readme docs with freaking emojis everywhere I have to stop using them.

Mildly annoying.

Check out this one I came across earlier - https://github.com/Jtensetti/fediverse-career-nexus/blob/main/README.md

It's a federated LinkedIn. ofc it's vibe coded.

Heard, not used though. Jokes about isEven(tm) too, but I never thought it goes like this in anything intended for external use

Nah, dependency hell is when two things you want to use depend on same thing, but different versions. The depth of dependencies needed to make "this one thing" work may or may not be a problem