When Fable 5 is used for frontier LLM development, it does not notify the user and instead limits the model’s capabilities through methods such as prompt modification, steering vectors, and PEFT. Anthropic estimated that this would affect approximately 0.03% of traffic.

wondering why I feel exhausted. maybe: the agents do all the easy stuff, and I have to work through the leftover hard bits, which means I'm perpetually locked in. and as the models get better, "my" work just gets harder and harder, until I'm basically underqualified to do the work (which... is better than the alternative, there's nothing left for me to do, and I'm paperclipped).

The partnership of a lifetime.

The partnership of a lifetime.

Just saving this here to document a story and as a self reflection on whether AI is really making me more productive Yesterday morning I found a way to complete the new HVM approach, that is much faster than before. I spent a few hours writing a spec, and then used Opus to implement. About 3k lines of C code later, everything worked and performance was incredible: 5x faster than HVM4 (stable at ~10x now). So, in one day I had outclassed HVM4. Incredible. I'd never have implemented that so fast manually. Now, enter today. I want to turn this into a real thing, but I haven't fully read the 3k lines yet. So, how do I trust it? I spent the whole day auditing the code. With AI. Several bugs found, most minor like forgetting to collect() some argument. But then I stumble upon this: λ{ inl: 1 ; inr: 1 } This was a test. But wait. This is matching on inl/inr. So the branches should receive the value of the Either. But they were numbers instead. Numbers aren't functions. This makes no sense. So why this is a test? It then stuck me. The AI completely misunderstood how function arities work. It literally assumed for no good reason that HVM5 was supposed to handle under/over-applied functions. For no good reason. I never wrote that. It never asked either. It just kinda thought "HVM is weird in some aspects, this might be one of them..." - and then it went on to implement a massive system to handle cases that should never happen to begin with. And all of that code is obviously wrong because it should not even exist. It is wrong. It is damage. And it is there. But it isn't too bad either. I just told Opus that it was wrong. Perhaps not so politely. And it solved it just fine. But then this begs the question. I spent ~20 hours in this file, and it is STILL not done. I went from 0 to 95% in the first 5 hours. Yet, 15 hours later, it is still not 100%. I suppose that is the real effect of using AI. If I had just written the C file manually in the last two days, would I not be further than where I am *right now*? Surely, the first version would have taken much longer to drop. But when I'd finish writing all that code, there would be zero, literally zero retarded shit. And, just today, I caught 5 or 6 retarded shit. And the worst part is: I don't know what the number of retarded shit left is, but I'm afraid it is >0. So if I have to read it all, review it all to ensure there is no retarded shit... what did I achieve by using AI, other than that dopamine anticipation?

Wild stat: when Cerebras went public they made 800 millionaires. Incredible. 🤯

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

The world really is a museum of passion projects. Almost everything that looks obvious now began as someone’s obsession. A hospital. A fund. A bank. A university. A public company. Someone carried it before the world recognised it. And the carrying is ugly.

“Tonight, at my direction, brave American forces and the Armed Forces of Nigeria flawlessly executed a meticulously planned and very complex mission to eliminate the most active terrorist in the world from the battlefield. Abu-Bilal al-Minuki, second in command of ISIS…” - President Trump

I thought maybe it's a banking industry problem so got chatgpt to look at other NGX vericals and banking seems decent. The manufacturing guys are dismal. We really do have a low pay problem in our economy. Wonder how govt policy can intervene. Shareholders are overcompensated.