It seems that longevity and extending healthspan are hot topics again. Sure. Who doesn’t want a long healthy life? My thinking is to stick to the basics and do them well. Stick to the stuff that does not change and probably will not change, regardless of new fads, fashions and “scientific discoveries”. Stuff like: Clean eating Eat whole foods Eat protein Eat fresh fruit and veg Time restricted feeding (1pm-6pm), no snacking Skip junk, simple carbs, processed foods Skip drugs and alcohol Daily exercise Walk 5km 7x/week Lift weights 3x/week Run 5-10km 3x/week Sauna 5x/week Sleep (3-2-1 framework) No food 3 hours before bed No drinks 2 hours before bed No screens 1 hour before bed (e.g. read) Early to bed, early to rise And more obvious stuff like: low stress, low pollution, etc. ...

I was reading “The Art & Business Of Ghostwriting” by Nicolas Cole last night and was reminded of the rule of ones. The chapter was on the topic of “How To Niche Down As A Ghostwriter” and he describes a framework for niching down that involves 1 problem, 1 person, and 1 way. Here’s the quote: 1 Specific Problem: You can’t solve all problems for all people. Instead, you are much better off picking 1 Specific Problem and saying, “This is the only thing I do.” Why? Because when you specialize in solving 1 Specific Problem, you get to know how to solve that problem better than anyone else—which allows you to charge more (you’re the best, so you have the pricing power), and which allows you to create efficiencies for yourself (meaning you can do more work in less time). You get to be so good at solving this 1 Specific Problem that you can do it in your sleep. 1 Specific Person: When you get ultra-specific about who you help, and in what industry, three things happen. First, it’s exponentially easier for clients to find you (you aren’t a “window repair specialist,” you’re a “stained-glass window repair specialist”). Second, they immediately trust you (because you are saying, “I specialize in helping people exactly like YOU”). And third, because you only work with 1 Type Of Person, in 1 Specific Industry, you are going to know way more about the problems these people face, and the outcomes they value most—far more than all the other “I can write anything for anyone!” generalists. 1 Specific Way: Saying you specialize in solving 1 Specific Problem for 1 Specific Person is 90% of the game. But what pushes your offer as a ghostwriter over the edge (and makes people want to throw money at you) is by solving that 1 Specific Problem for that 1 Specific Person in a new, unique, and different way. I wrote about this in my book, Snow Leopard, explaining how the key to differentiating yourself is to solve Obvious problems in Non-Obvious ways, or to solve Non-Obvious problems in Obvious ways. It’s a good chapter, although could probably be condensed to one page (but shouldn’t). ...

After learning the basics of programming, we learn algorithms and data structures. I have very fond memories of implementing many algorithms and data structures in ANSI C and Java in the late 1990s. There was something very pleasurable in implementing well-known and well-defined computational concepts and then playing with them. Specifically, data structures like: arrays linked lists hash tables graphs stacks queues trees and on And algorithms like: binary search bubble sort quick sort heap sort breadth-first search depth-first search best-first search a-star search and on… I don’t think I’ve implemented any of these algorithms and data structures since. But there was a time in the late ‘90’s where that is all I was doing for months and months. Perhaps years. ...

I was thinking about LLM as a recommender system. I wrote about this the other day, how they can give a different type of recommendation. It occurs to me that we can use LLMs to give better recommendations. I want this for books. But, LLM recommendations are different how? That’s what I was thinking about. So, the most basic recommendations are: Other books by the author Other books in genre The most popular recommender systems for the last 15+ years are collaborative filtering. ...

I’ve often thought/said/repeated that I “learn best by programming”. It seems true. The process is typically something like: pick a concept read enough about it to get the gist implement it in code it doesn’t work iterate: reading other sources and updating the code until it works share a tutorial on the concept taught via a worked example in code (bonus!) I did this during my phd (60+ tech reports), when writing my first book (~50 optimization algorithms), with machine learning mastery (1000+ tutorials, 20+ books on machine learning and deep learning), and with super fast python (500+ tutorials, 14+ books on python concurrency), and on. ...

I’m currently reading Reid Hoffman’s new book “Superagency”. In an early chapter, he condenses and summarizes the main ideological AI camps into 4 categories: So far, at least four key constituencies have been informing the discourse: the Doomers, the Gloomers, the Zoomers, and the Bloomers Here’s a definition of each in turn (summarized by gpt4o from a quote taken from the book): Doomers – People who believe AI poses an existential threat to humanity, especially if superintelligent, autonomous AI systems become misaligned with human values. They fear worst-case scenarios where AI could decide to destroy or enslave humanity. Their concerns often center on long-term risks, including AI-driven human extinction. ...

I tripped over an ideation framework by John Rush shared in a tweet. Here is a local copy (tweets get deleted sometimes…): Here’s the tweet where it came from: My ideation framework: (I used it to launch over 20 startups) pic.twitter.com/T0SwdBHLhL — John Rush (@johnrushx) January 23, 2025 It’s a great framework. Here’s a snippet of what it means, as described by our friend gpt4o: Start with pain points as the best source of high-quality ideas. Consider technology as a way to adapt existing solutions or create new applications. Cloning can be a valid strategy but typically lacks originality unless combined with significant differentiation. Thoughtfully evaluate ideas’ potential impact, innovativeness, and market fit. Almost gpt buddy, good try. ...

For about 20 years, I’ve been obsessed with the idea of getting the best score for a machine learning dataset. It started in postgrad when we would talk about learning algorithms and the problems and datasets we were all using to demonstrate that one algorithm was “better” than another. A good friend in our research group always pointed out Włodzisław Duch’s work. Back in the early 2000s, he had a website that listed a suite of the standard ML datasets and the algorithms + configs that achieved the best scores (as reported in published papers), and most importatnyl, the scores they achieved. ...

I don’t watch a ton of TV anymore. And I’m not a consumer of youtube. Never really liked it. Nevertheless, I like to watch one, and only one, hour of a tv series (or a contiguous block of a movie) as part of each nights wind-down routine. I’m a huge consumer of podcasts. I’ve been consuming podcasts of movie reviews for a long time. Mostly movies I watched a long time ago. Often movies I won’t see and just want to get a vibe for the plot and quality. ...

Andrej Karpathy has a wonderful tweet on what he calls learned “cognitive strategies” but I think more generally is referred to as “meta cognition”. The piece I like is: …The models discover, in the process of trying to solve many diverse math/code/etc. problems, strategies that resemble the internal monologue of humans, which are very hard (/impossible) to directly program into the models. I call these “cognitive strategies” - things like approaching a problem from different angles, trying out different ideas, finding analogies, backtracking, re-examining, etc. Weird as it sounds, it’s plausible that LLMs can discover better ways of thinking, of solving problems, of connecting ideas across disciplines, and do so in a way we will find surprising, puzzling, but creative and brilliant in retrospect… ...