Drdiffie retweeted
Elon Musk's first child, Nevada Alexander Musk, died of sudden infant death syndrome (SIDS) at 10 weeks old. The baby stopped breathing. Justine Musk has described the loss as the moment that changed Elon permanently and irreversibly. He held the baby as it died. And then he went back to work.
Justine has written about the period after Nevada's death with a precision that makes the grief tangible. She described a man who processed the loss by refusing to process it. He did not take time off. He did not go to therapy. He did not sit with the grief. He returned to work and operated at full capacity within days. The external function was uninterrupted. The internal damage was never addressed.
She wrote that when she tried to talk about Nevada, Elon told her that dwelling on things that cannot be changed is not productive. The statement is logically correct and emotionally devastating. The partner who had just lost a child was told by the other parent that discussing the loss was unproductive. The word "unproductive" applied to the death of a 10 week old baby.
They conceived twins through IVF shortly after Nevada's death. The decision to have more children immediately was Elon's. The logic was the same logic he applies to everything: a loss was incurred. The response is to rebuild. Not to grieve. Not to wait. Rebuild. The twins were born 2 years after Nevada's death. The rebuilding was biological. The grief was unprocessed.
Elon has almost never spoken about Nevada publicly. When asked, he gives brief, factual answers without emotional elaboration. The absence of elaboration is itself the story. A man who can describe rocket physics for 40 minutes cannot describe the death of his first child for 40 seconds. The topic is the one system he cannot debug by talking about it.
14 children followed Nevada. Each pregnancy was rapid. Each child was produced with the urgency of someone replacing something that was taken. The question that nobody asks is whether the man who produces children at a rate that exceeds any reasonable parenting capacity is still, at some level, trying to replace the first one who stopped breathing in his arms at 10 weeks.
The grief was never expressed. The children kept coming. The companies kept building. The man who solves every problem by building moved to the next build before the last loss was processed. And 20 years later the loss is still unprocessed because the man who processes everything cannot process the one thing that processing cannot fix.
Drdiffie retweeted
when a cracked autist looks at you like that and starts dropping details about their plans... believe them
Drdiffie retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Drdiffie retweeted
me: let's do some reverse engineering
gpt-6-astra: i won't do that.
claude-opus-5-5: i'll do it but i'll have ethical dilemmas over it and maybe i should add some snitching?
glm-5.3: OK LETS DO SOME MITM FIRST
Drdiffie retweeted
Remember, "there is no fire alarm for artificial general intelligence." When the singularity does come, it will start with seemingly mundane feats (if you can even call this that). Now really does feel like the beginning of that. intelligence.org/2017/10/13/…
Drdiffie retweeted
Berlin um 2 Millionen Euro zu erpressen, ist wie den Penner hinterm Aldi zu fragen, ob er dir 'nen Hunderter kleinmachen kann.
Drdiffie retweeted
If you shuffle a pack of cards, it's likely that you have come up with an ordering of cards that is unique in human history. In fact in a 52 pack of card, there are
80,658,175,170,943,878,571,660,636,856,403,766,975,289,505,440,883,277,824,000,000,000,000
possibilities.
Drdiffie retweeted
1 engineer + Claude Code got more of Node.js’s test suite passing over the last month than the entire Bun team (me included) did in the 9 months from 1.2 → 1.3
Bun v1.4 will be our biggest jump in Node.js compatibility since Bun v1.0
ALT Line chart titled "Bun's Node.js test suite progress", subtitled "Official Node.js test suite files run on every commit". The line starts at 1,378 test files at Bun v1.2.0 (January 2025), climbs through the 1.2 series to 2,250 at v1.3.14, holds flat across the 1.3 releases, then spikes to 3,188 files on main today (July 2026). A dashed target line at 3,693 reads "The bar to ship Bun v1.4: 3,693", with "505 to go" marking the gap between main and the target.
