This behavioral change is actually official (https://www.anthropic.com/news/redeploying-fable-5):

> For Fable 5, we made this safety margin much larger than in any prior launch (row B), meaning that many more benign requests would be blocked. We understood that these kinds of false positives would be frustrating for users, but made this tradeoff in the interest of making the model’s other capabilities widely available.

Scott Miller said it himself:

> Big day today at Id Software [...] today, Microsoft/XBOX decided half the team was deemed USELESS and needed to be let go [...] With literally the best of the best coders in the industry.

Learning LLM internals != using them as daily tools (the latter was the topic of discussion).

Shifting to the learning topic: no need for expensive hardware. Even a 16 GB GPU will do, and that's (relatively) cheap and available.

The steps, described [here](https://github.com/obra/superpowers#the-basic-workflow), are: brainstorming → using-git-worktrees → writing-plans → subagent-driven-development or executing-plans → test-driven-development → requesting-code-review → finishing-a-development-branch.

The principles, described [here](https://github.com/obra/superpowers#philosophy), are: Write tests first, always; Process over guessing; Simplicity as primary goal; Verify before declaring success.

Install it, take a complex tasks, and instruct the agent to implement it; it's easier to watch it in action than to describe it.

In my own experience, the advantage is that it's a very systematic workflow - investigation of requirements, breakdown in simpler steps, and TDD development, among the other aspects.

No!! This is misinformation.

An LLM predicts the next words (tokens) based on the words before it *and* on the internal representations it has learned from training.

Processing language can lead to internal representations that capture patterns and relationships; LLMs can exhibit emergent abilities, including reasoning-like (stress on "-like") behavior, even when they weren't explicitly trained for that.

Making reductionist claims about LLMs, based on next-word prediction, is similar to making such claims about biology, based on amino acids.

Cloud models are (much) faster, they don't consume so much power/generate heat, they have much bigger (LLM) context, they're much more precise and they have a much wider (engineering) context of the given problem.

Except privacy and use cases that are blocked by cloud models (e.g. reverse engineering), local LLMs are currently an expensive toy.

When I try to program with a local LLM (I'm on a 32/128 GB system), I end up wasting time compared to a cloud LLM.

It's not that the company was ruined, but that it had lost some of its creative direction after Romero left (while retaining technical excellence).

32 CPU Epyc (Epyc is required for faster memory access) + 32 GB VRAM + 512 GB RAM is stupid expensive nowadays, and in best case, it will just downgrade to "very" expensive at some point in the future.

This makes sense only if 1. one is paranoid about privacy or 2. they have money to smoke or 3. they need to workaround cloud model restrictions, AND they have to do it routinely (because if not, a oneshot cloud bare metal setup is way cheaper, faster, and allows more powerful models, due to VRAM offering).

I did spend stupid money as well and yet, the system is 2x slower than cloud providers for comparable performance on vision tasks (I still have to test coding). Oh, and it's hot as hell.

If you read the article, you'll find that the it indeed relies on this claim:

> Within a couple of years, possibly much sooner, AI may achieve so-called closed-loop recursive self-improvement (RSI): the capacity to rewrite its own code to become more capable, without human intervention. Should that happen, the result could be an intelligence explosion of a kind for which there is no precedent and no map.

This may be optimistic and/or simplistic, but not impossible.

Just out curiosity, PoP ran on 8088/8086, while Flashback on 286/386.

There's nothing in the market like them, which is a shame - I think a slightly better quality Minibook (Chuwis are plain crap) would be a very solid laptop.

There is an option already, at least from AMD, in the HEDT segment - Threadripper/Pro has 4/8 channels (although the bandwidth is not a high as Apple chips).