If you look at those things, they're kind of just bad programming practices. I I don't really know how else to say them. They don't mesh well when you put them together.
I wish everybody read this book. The surprising thing is that many aspects and recommendations apply to virtually any language, not specifically English.
I truly believe these these models are already just incredibly powerful. Like they should they should be so powerful to fuel, you know, um many many points of expansion of GDP growth and I think it's like up to smart people with vision and ambition to make all that happen.
Those first jetliners truly did double the speed of what had come before them. And was they doubled the speed of air travel, the most important changes were on the ground. Hawaii was simply not a tourist destination before the speed of the jet.
part of the goal of programs is to communicate intent to other human beings and now to models as well which which is a much more open-ended problem. We understand a lot more about how to communicate to other human beings whether we apply that understanding or not. We don't understand at all how to communicate effectively to models
and then the power and the cost and then you can double the performance but you cannot double down on the cost and and area. So those are the thing you have to give give way unless you find some new way of material new way of design.
the point is that where we always get bottlenecked is where the the previous processes and and and heuristics don't apply. Right? Like that's almost sort of definitionally what causes the bottlenecks.
it required a different way of thinking and the cognitive load might be a little higher but the output is dramatic and we have seen our best engineer double their output.
a lot of companies haven't started until now thinking about how we could apply AI to the very human, very business process part of it. And so, that will keep slowing us down until we find a way to to address it, right?
Uh but whenever we do a systematic study, um any given problem, an AI tool has a success rate of maybe 1 or 2%. Uh it's just that it's just that they can apply at scale and and you just pick the winners, it looks great.
well the model the compute efficiency gains you get from research are so large you actually want most of your compute to go to research not to development because you know all these researchers are generating new ideas trying them out testing them and continuing to march along this and push the prao optimal curve of scaling laws further and further and further
Because Enthropic saw it before Google. And then Google had Nano Banano and Gemini 3 which caused their user metrics to skyrocket and leadership at Google was like oh and then they started making the statement of we have to double compute every is it 6 months or I don't remember the exact number that they said.
Basically what that gives you is like you can apply a lot of theoretical sort of um a lot of theoretical toolkit used in physics to model parts of this question in ways that are actually useful. And it is just not true that you can use like equations from physics to think usefully about almost any other problem in biology.
The Academy Is…, one of my favorite bands from this century (and yes, I feel old just typing that out), has recorded their first new album in eighteen years, titled Almost There, and will be putting it out in March.
Try a number of things, right? So, experiment with a number of things. Run a number of small experiments. Once you find something that works, double down on it and then keep doing it until it stops working, which is the step that a lot of people skip. Um, and then once it stops working, go back to the start and try a lot of small experiments again.
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.
I think basically what takes the long amount of time and the way to think about it is that it's a march of nines and every single nine is a constant amount of work.
The bitter lesson. Oh, who cares about that? That's that's an empirical observation about a particular period in history. 70 years in history no longer doesn't necessarily have to apply the next 70 years.
Like it seems to me right now you could do like a double blind test of the same prompt given to Grock Claude Gemini um Mistral Deep Seek. Do a double blind test. I bet most people wouldn't be able to tell which is which.
While the same does not directly apply in other fields, working with others to produce the best results for everyone will be much better in the long-term than focusing solely on what Amazon needs right now.
My model is that research requires a mix of skills. The day-to-day coding and execution is crucial. But there's also a set of harder-to-learn conceptual skills, collectively called research taste. These skills take a long time to gain because they have poor feedback loops, but they take very little time to use.
You already bought that phone, why should Apple be adding a 30% junk fee to all commerce you do, and why do they selectively apply it to some things and not others? I've always viewed this as deeply abusive and that it shuts down the competitive engine that once fueled the app and software economy.
The journey is better than the destination. Everyone's heard this. Just take one second to apply what that means. That means forever starting from now, you are only going towards a place that's worse.
(publication date March 18, 2025, Viking) by Laura Delano is a compelling and troubling memoir of psychiatric patienthood, iatrogenic harm, and finding a meaningful and flourishing life outside of the mental healthcare system.
So, Japan has a law which you're allowed to train on any training data and copyrights don't apply if you want to train a model, A. B, Japan has 9 gigawatts of curtailed nuclear power. C, Japan is allowed under the AI diffusion rule to import as many GPUs as they'd like.
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.
An eye-opening view on considerations going into building a widely used public API or reusable library. While the book focuses on the .NET framework, many of the conventions apply to maintainable and reusable components, in general. This book had an outsized impact on me as I read it when I was a mid-level .NET developer.
I think if we can achieve that amount of inference compute, where it leads to a dramatically better answer as you apply more inference compute, I think that will be the beginning of real reasoning breakthroughs.
That is, labs should make sure that the safety measures they apply to their powerful models prevent unacceptably bad outcomes, even if the AIs are misaligned and intentionally try to subvert those safety measures.
These enormous costs are ultimately due to the same factor that has steadily driven down the cost of semiconductors: Moore’s Law, the observation that the number of components on an integrated circuit tends to double every two years.
a new relationship is like fresh powder. It is new and shiny and exciting but it will not be a new relationship for long. And then you're still stuck with an old relationship and the question is will your old relationship a year from now be better than your old relationship that you currently have is? And if the answer is yes then yes sunk costs completely apply. But if the answer is no then what you're really doing is shopping for novelty not ignoring sunk costs.
So abstract. Maybe the most abstract book you’ll ever read. Compares “finite” games with rules and winners, versus “infinite” games without winners where we can play with the game itself. Is it about a job versus a calling? Religion versus spirituality? A story versus story-telling? Who knows. Thought-provoking if you can apply the metaphor to whatever concerns you.
my central thesis about the world is there’s things that centralize power, and they’re bad. There’s things that decentralize power, and they’re good. Everything I can do to help decentralize power, I’d like to do.
people get up to a certain level of performance, and then they start to stagnate. And they start to plateau. And people who who break through and move to the next level um are the ones who have to who engage in unlearning. Who realize there are certain uh conventions they've bought into or beliefs they hold that are either wrong or are limited and and don't apply as broadly as as they imagine.
Adam Rutherford and Hannah Fry, The Complete Guide to Absolutely Everything – just delightful, wide-ranging stuff from the surely best science-communication double-act in the world. The perfect gift for the nerd in your life.
If you want one book to teach you everything you need to know about giving great presentations, this is the book. A film director, a psychologist, and an actor share their combined experiences to help improve your content, the design, your movements, and just about every other detail of being a great speaker. It was a slow start, but by the end, I had dog-eared dozens of pages and marked up many more which I was able to directly apply to a major talk I was working on.
This is a great book for understanding how the creative process works. It left me with a lot of ideas I could apply to my day to day to be more creative. After living in the world of startups and business in real life and most of the books I read, it was great to hear how the different world of dance performance can be learned from.
If you're curious how Google built it's culture and the processes thanks to their extremely data & engineering driven nature, this is a great book. What held it back was being ~100 pages too long and Bock didn't always understand where what they did only fit at a $100Bn+, mega-profitable company. Still, the insights on the studies on their 50,000+ employees is well worth the read since so few of us can get similar statistical significance to apply to our teams.
It goes much further and deeper into how to approach actually doing Lean in your business than Eric Ries's The Lean Startup book. It also has some awesome case studies showing how lean can apply to any industry. See my full review here.
Simple, practical, useful. This book is a quick read that covers modern positioning and how to do it. Unlike all the theoretical books out there, this is a VC who lived a life as a marketer (all the way up to CMO/VP) doing this and then worked with more entrepreneurs. This hands on experience leads to a system that is easy to understand and apply. I'm now trying to apply it so this score will rise or fall based on the results we have.
This is what I wish I learned from in my freshman year of college. The part of it about linear algebra is one of the best linear algebra resources out there, and remainder shows you how those tools apply to non-linear mathematics.
Add these up, and it’s plausible that solar’s contribution to the energy system could double or triple the amount currently considered feasible for solar to provide.
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