draginol

I think you could argue that this is following the same trend as forums (and usenet before that). You get a consolidation of where people go to read up on things that interest them.

Look at Slashdot for example, it was once so popular that any site it linked to could be "slashdotted" from all the traffic. Now people go elsewhere. YouTube, TikTok, Reddit.

With all the uncertainty about AI regulation, I don't think now is the time.

How do you even value a company when we don't even know if GPT-6 will be made available to the general public?

So this is probably not good news for the MacBook Ultra with 512GB of RAM rumors being..affordable.

What's worse is that this is probably going to get worse. My angel investment group is getting inundated with pitches that amount to building an RX-6000 with 96GB of RAM and installing a local model to do "thing X".

So even if the OpenAI's of the world stop trying to use up all the RAM, you're going to have thousands of start-ups pushing local models.

This is what we are finding a lot with the "AI normies". Because the AI responses are so confident new users of it think it must be correct.

AI is confidently wrong a lot. And so you can imagine a lot of execs thinking the AI can do a lot more than it really can.

I think he means where the DOS/config is all set up too. Sort of like a really small VM.

Eh. I don't think "developers broke it". The 8086 gave us something like a flat 20-bit address space and then encoded it as a segment. Once that exists, normalizing far pointers is inevitable.

Not to do a "The Amiga was ahead of its time" thing but as a reminder, 68000 has a flat 24-bit address.

The most interesting part of this to me is not the benchmark table, but the packaging.

A model like GLM-5.2 being available as GGUF, usable through llama.cpp/Ollama/vLLM/SGLang/LM Studio, and wrapped for local agent workflows changes the category. It stops being an impressive open model exists and starts becoming this is something a small team can actually put into its development stack.

For instance, company buys an RX6000 setup for say $15k total. They could use this for handling data heavy sifting that would otherwise be a lot of Claude tokens.

It doesn't need to be as good as frontier-best. Just good enough.

I could see a business of people packaging this and handing it to companies who want Help Desk bots without any extra setup.

This is really terrible advice right now for most people.

I've had to rip out a lot of pretty terrible code made by engineers who have tried this.

I don't disagree that eventually, "loops" when combined with unlimited tokens and amazing models in the hands of people who know how to set them up right will be amazing. But for the typical Claude Code user, it's disaster.

The problem is not that loops write bad code once. Humans do that too. The problem is that loops apply local pressure repeatedly: add a fallback, add a guard, special-case the failing input, quiet the exception, satisfy the test. Over time that selects for code that is more survivable in the short term but less intelligible in the long term.

I'm more happy to see C++ moving to consolidate around a single formatting model.

This past year we were porting Elemental (PC game) to 64-bit so it's pretty old code. There are a gazillion different string types in it (sprintf and beyond).

I was pretty impressed with Fable when I used it. Fable on Low was better than Opus 4.8 on High (and cheaper).

Now, for me, it was really about how well it worked on big existing human made code bases. I was working on some new screens in GalCiv IV and if you've ever had to make screens for games, it is incredibly tedious, low brain work. But GPT 5.5 and Opus 4.8 would just struggle with these over and over again and this is C++ work with limited hotloading so it's a slow process. Fable nailed these screens fast.

With the scalping issue, the issue isn't whether can we detect scalpers to which anti scalping mechanism has the lowest false positive cost to it?

The random reservation order takes the scalping issue out of the fulfillment part and into allocation making it a lot harder on the scalper.