The answer to every significant programming challenge in 2026 is a Python library you didn't think to go looking for because it's in Python and not Rust, as if that mattered anymore. Kicking myself every time I realize this.
Inside Anthropic and OpenAI, internal models are improving even faster. Over the summer there was a step change, as Mythos and Astra started kicking off the early stages of recursive self-improvement (RSI).
The answer to every significant programming challenge in 2026 is a Python library you didn't think to go looking for because it's in Python and not Rust, as if that mattered anymore.
And I do believe Atlas is a significant step forward because now with every single frame, you have a you can generate an estimate a important piece of information, which is the the view viewpoint, the camera pose. And that is the most critical information one needs about the geometry of the of the space.
so I'm in the camp that like continuity is greatly important. And so people will accept significant drift in their identity over time as long as they have continuity.
The catch is that the future world that has been warmed by greenhouse gases and a present-day world that has been temporarily warmed by El Niño can have the same global-average temperature while having significant differences in the impacts of this warming.
So, in general, I think it's not good in icons to show pictures of a particular product design because like the 3 and 1/2 in floppy for save. Um even though some there's still a few of those kicking around, but obviously it it didn't age well. Better to use a metaphor.
my sense is that like AIs are a worse co-orker than a human in terms of how much of a scumbag they are. Like at least this like this has been my experience as of the start of the year and I think it's still you know true to a significant extent now where the AIs are much more likely to like pretend they did the task when they actually didn't. sort of like misleadingly suggest they did things when they actually um you know did them much more poorly um and be like pretty sloppy without drawing attention to ways in which they're sloppy.
cognitive function doesn't just decline sort of linearly or steadily with age. It goes down, you know, a little bit on average, but the biggest decreases sort of happen stepwise after periods of significant illness.
You look under the underneath though at the code and it was just a horrible unmaintainable mess. But the fact that people could program the programs that they wanted was a a significant step forward as opposed to I'm going to write a thousandpage requirements document and then wait 8 years and not get what I want which was the alternative that we were offering at the at the time.
One is of course everybody knows power constraint. Some country the power they just don't have that. They get impacted. And then secondly, a lot of people didn't realize the helium impact can be also very significant for semiconductor. And then the thirdly, is everybody know right now memory is a bigger shortage.
The war in Ukraine has now lasted longer than the First World War, and there is every reason to think that its outcome will be of comparable historical significance.
If AI-driven labor displacement ends up being large in magnitude and permanently drives down the demand for labor, it will likely be necessary to go beyond mere incentive programs to long-term income support for a significant fraction of the labor force.
what really mattered was connecting you to the right people. And so, if you could just connect someone not to all their friends, but to their best friend, to their partner, to their spouse, the people that they cared most about in the world, that that that's where the majority of the value is in the network.
It's more that for myself, the process of writing is the way how I figure things out. And figuring things out is really my goal here. So, I'm I'm trying to figure it out in my own heads, and for that I just have to write it myself.
They could release claw slow mode and have an increase in tokens per dollar by a significant amount. Um they could probably like reduce the price of Opus 46 by you know 4x 5x and reduce the speed by another by maybe just like 2x like the curve on inference throughput versus speed is there already just on hm um and yet they don't um because no one actually wants to use a slow model
I can say without a doubt that Superhuman is the best tool I’ve ever used to work through email quickly. There’s no single reason. The keyboard shortcuts are lightning fast. The composition tools avoid friction. Its auto-sorting is excellent. I’m just scratching the surface of the full feature set. But Superhuman comes with a significant cost at $30 a month.
GPT 5.2 goes till end of August whereas Opus is stuck in mid-March - that’s about 5 months. Which is significant when you wanna use the latest available tools.
And if I'm starting to see like more and more experiments that are not statistically significant, that may be a signal to me to say, okay, we might have kind of tried to exploit a little bit too far. Like there might not be as much juice to squeeze.
The panel had a generally positive view of Braintrust, highlighting its clean UI and structured approach to evaluations. The tool’s emphasis on human-in-the-loop workflows was a significant strength.
And when you make a mistake and correct it, well, first you you've achieved the task because you've corrected, but you've also gained knowledge that allows you to avoid that mistake in the future. With driving, because of the dynamics of how it's set up, it's very hard to make a mistake, correct it, and then learn from it because the mistakes themselves have significant ramifications.
I personally hate meetings because a significant percent of meetings when done poorly don’t serve a clear purpose. But that’s a meeting problem, that’s not a communication problem.
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
Humans being awful drivers leads to a massive increase in vehicle costs and weight, makes cars more dangerous to people outside of them, and imposes significant costs on infrastructure like roads.
is this idea that we form these internal models at particular points of high prediction error or points of, I believe also points of uncertainty, points of surprise or motivationally significant periods. And those points are when it’s maximally optimal to encode an episodic memory.
That’s a theatrical risk. That is a thing that can really take over how people think about this problem. And there’s a big group of very smart, I think very well-meaning AI safety researchers that got super-hung up on this one problem, I’d argue without much progress, but super-hung up on this one problem. I’m actually happy that they do that, because I think we do need to think about this more. But I think it pushed out of the space of discourse a lot of the other very significant AI- related risks.
The most significant factor preventing us from taking better molecules into clinical trials is not our ability to design arbitrary molecules, but our (lack of) understanding of underlying disease biology. In other words, we often know how to make something, but not what to make.
I view Keynes’s bisexuality and his strong interest in gay sex as a significant cause of what made him interesting and which gave him so strong an ability to grasp different and outside perspectives.
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