Self-hosted Atlassian products seem to be just fine.

As for what "they said", they also said this cloud shit would be cheaper. It isn't.

Wikipedia is an interesting concept and it works decently well as a place to go read a bunch of general information and find decent sources. But LLMs are feeding that information to people in a customized, granular format that meets their exact individual needs and desires. So yeah, probably not as interested in reading your giant wall of text when they want 6 specific lines out of it.

Remember when Encyclopædia Britannica was crying about you stealing their customers, Wikipedia? Yeah, this is what they experienced.

If you spend time with the higher-tier (paid) reasoning models, you’ll see they already operate in ways that are effectively deductive (i.e., behaviorally indistinguishable) within the bounds of where they operate well. So not novel theorem proving. But give them scheduling constraints, warranty/return policies, travel planning, or system troubleshooting, and they’ll parse the conditions, decompose the problem, and run through intermediate steps until they land on the right conclusion. That’s not "just chained prediction". It’s structured reasoning that, in practice, outperforms what a lot of humans can do effectively.

When the domain is checkable (e.g., dates, constraints, algebraic rewrites, SAT-style logic), the outputs are effectively indistinguishable from human deduction. Outside those domains, yes it drifts into probabilistic inference or “reading between the lines.” But to dismiss it all as “not deduction at all” ignores how far beyond surface-level token prediction the good models already are. If you want to dismiss all that by saying “but it’s just prediction,” you’re basically saying deduction doesn’t count unless it’s done by a human. That’s just redefining words to try and win an Internet argument.

I'm finding more and more hallucinations coming from AI every day. I asked multiple LLMs for a configuration file for some software, and they all made stuff up that didn't exist. For example, one result told me to use a specific plugin to achieve what I wanted because it was designed just for that purpose. Problem was, that plugin doesn't exist. Even the same LLM would come back and tell me there was no such thing.

They do quite a bit more than that. There's a good bit of reasoning that comes into play and newer models (really beginning with o3 on the ChatGPT side) can do multi-step reasoning where it'll first determine what the user is actually seeking, then determine what it needs to provide that, then begin the process of response generation based on all of that.

This is not a surprise, just one more data point that LLMs fundamentally suck and cannot be trusted.

Huh? LLMs are not perfect and are not expert-level in every single thing ever. But that doesn't mean they suck. Nothing does everything. A great LLM can fail to produce a perfect original proof but still be excellent at helping people adjust the tone of their writing or understanding interactions with others or developing communication skills, developing coping skills, or learning new subjects quickly. I've used ChatGPT for everything from landscaping to plumbing successfully. Right now it's helping to guide my diet, tracking macros and suggesting strategies and recipes to remain on target.

LLMs are a tool with use cases where they work well and use cases where they don't. They actually have a very wide set of use cases. A hammer doesn't suck just because I can't use it to cut my grass. That's not a use case where it excels. But a hammer is a perfect tool for hammering nails into wood and it's pretty decent at putting holes in drywall. Let's not throw out LLMs just because they don't do everything everywhere perfectly at all times. They're a brand new novel tool that's suddenly been put into millions of peoples' hands. And it's been massively improved over the past few years to expand its usefulness. But it's still just a tool.

And their partner is a 0xBEEFBABE

If/when true AGI is achieved, only a fool would announce it. What would announcing it do for you? Make you famous? Rich? Cool. Know what's better than all that?

Not telling a damn soul and using the AGI quietly to do whatever the Hell you want. If you want to be rich, the AGI will tell you how to become rich. If you want to be famous, the AGI will tell you how to become famous. You can do both. And you don't have to stop there. A real, vastly superior AGI enables the person controlling it to do anything. The second you tell people about it, you'll lose control over it and then you're the famous idiot who did a cool thing one time. Kids in elementary school will recite your name back on a test. And you could have had everything.

Anyone smart enough to crack AGI can't also be stupid enough to advertise when they do it.

That whole "the IRS is targeting conservatives" rhetoric is pure propaganda. Reality is that the IRS targeted people who publicly posted things like "the IRS is unconstitutional" or "I don't believe in paying income taxes". It shouldn't be a surprise that most of them are right-wing nutjobs.

I read it as 4.5 didn't end up performing as well as hoped in the real world and 4.1 being a direct iteration of 4o rather than 4.5.

They're operating with parallel code lines because each is targeting different use cases.

These are all issues directly related to regulation and unnecessary red tape created out of NIMBYism and irrational fear around radiation. India, Canada, and China aren't stupid. They're building and/or modernizing nuclear power plants like crazy because they're so effective at reliable baseline power, which renewables simply are not. In the US, we force years - sometimes decades - of reviews and permits and defending court cases and other bullshit unrelated to the construction and operation of clean, safe nuclear power.

The other issue going to cost is that the US - again, stupidly - bars reuse of high energy spent fuel. If you simply separate the low energy (relatively safe, but useless for generating power) waste from the high energy fuel remaining and feed the high energy stuff back in, you can extract nearly all the energy, save a ton of fuel costs, mine less fuel, and have vastly less volume of waste and vastly less energetic waste.

Let's assume some sort of absolute mandate were passed to build 5 CANDU-6 (known, proven, safe, reliable design) reactors. No reviews, no permits, no red tape, no lawsuits. Just build the damn things now. You can get one operational in ~3.5 years, all of them in about 4ish years. South Korea and China have built PWRs in 5. Assuming we also lifted the ban on fuel reprocessing, CANDU-6 plants will produce power at a cost of 5-6 cents per kWh, yielding a retail price of 13-17 cents per kWh. US average is about 16.2 cents, California has rates pushing 50 cents. But we're too stupid to get out of our own way and just do it, so we'll keep strangling the poor and middle class economically.

And for decades thereafter Republican politicians pushed for cheap overseas labor to help their corporate donors increase profits, and whenever Democrats objected to try to keep American factory workers employed, they were called "protectionist" as if protecting American jobs was a bad thing. Now the Republicans want to take credit for the notion of bringing back the jobs that they eliminated.

The first iPhone also had visual voicemail, which was completely new and innovative and a vast improvement over the existing approach.

It also introduced swiping and other gestures like pinch to zoom. Apple didn't invent these things, but Blackberrys and the original Android phones still used physical buttons for scrolling. I don't think the original Androids even had a touchscreen.

And you left out an absolutely absurd claim by the parent poster: "The last actual innovation that came from tech was a smartphone."

It's mimicked intelligence. You're absolutely right that - under the hood - there's not the sort of traditionally cognitive processing happening that we might consider intelligence. That can be a distinction without a difference if the output is the same, and for quite a lot of things, they're becoming indistinguishable.

I think the real challenge for LLMs specifically is the training data. Between the limits placed technically and legally, malicious poisoning already happening, and the breakdown of function seen when LLM generated content is repeatedly added to its training data (i.e., "model collapse"). However, I also think that by the time we start to see major effects of this, the LLMs of today will have evolved to largely work around this limitation and the underlying process for generating output will be far less susceptible to the problems seen today. Time will tell whether that's overly optimistic, but there's a ton of development in this space toward better approaches.