almost a decade+ ago while working in big tech i changed my visible title to "applied generalist" because this is basically how i thought about myself from the moment i was on the internet as a kid. i didn't jive with product management cuz i wasn't supposed to write code (ppl would look at you weirdly if you did this), & i hated the idea of being just an engineer because ppl would just treat you like a code monkey, & i hated being in design because ppl often just treated you as a pixel pusher. legacy companies (which i now include big tech as part of) are still organized around highly specific roles like these + levels, & career ladders. i.e. your position in the hierarchy determines not only what you actually do but also how ppl interact with you. i remember walking into meetings with VPs who didn't know how to treat me cuz they couldn't place me in a ladder due to my lack of an officially sanctioned title. & i absolutely hated this existence with a passion. being pigeonholed into a narrowly defined function felt like wearing a kind of straitjacket. this is why the member of technical staff movement with ai companies is so damn necessary. if you as a company do not move towards a more general model like this, you will absolutely get left behind & alienate ppl who can perform across the spectrum.

experimenting with a lil personal project: an AI-native macOS dock with custom modes (writing, building, etc.) to help me stay focused my agent is able to customize mode behavior and add new modes, it uses jev to classify my activity + notifications in real time & act on them

The real 1-person billion-dollar company. A solo smart, high-agency CEO + AI can have an outsized impact on the world.

It’s happening. RBI recommends the finance minister to eliminate the LRS outflow limit from $250K to $0 & lower it to $50K for education & healthcare spends. It’s all on predictable lines. No domestic money can exit India. Looks like it’s the HNIs taking $ out. Don’t blame FIIs

video creation and game making are going to become widely distributed foundations of knowledge work in the same way doc and deck creation are today

Martial artist and knife fighting instructor Doug Marcaida shows how lethal a 'karambit' could be in the hands of an expert.

This is really interesting. All of OpenRouter's Deepseek demand (roughly 30% of total OR token traffic) could be satisfied by 5-10 GB300 NVL72 racks. Even if that 10x over the next year, it's 50-100 racks. NVIDIA is shipping 90k racks next year? What am I missing?

We are really underestimating how efficient inference is at big labs. Anthropic's gross margin is 85%, so it costs them $0.60 / $3.00 to serve Opus 5.5. This is less than what OpenAI is charging for 6.1 Terra and much less than Muse, Gemini, Kimi etc.

new load-bearing internet comment just dropped

This man left Anthropic to play WoW Forever. Fking legend.

The architecture of a psychedelic collapse.

This is the key stat. The average Fortune 500 customer of Anthropic spends less than .1% of their expenses on Anthropic. While that number likely will eventually asymptote somewhere (token prices coming down, and other large enterprise spend areas not going away), there’s no reason it can’t be 10-50X higher than this, reaching a “mere” 1-5% of spend. Which would still be a fraction of what most startups are spending. Thus, you can see how big of a divide there is between the frontier users of AI and the rest of the world still. Today, only a microscopically small set of companies are actually using agents for large amounts of background tasks and inside of workflows that aren’t human prompted. This Fortune 500 spend is likely dominated by coding tasks and broad employee productivity use-cases. But the big jump will come as these enterprises start to rewire their underlying processes for agents, which we’re now in the earliest stages of seeing. Agents processing large amounts of data in the enterprises; swarms doing coding, bio research, cyber, and other large fan out tasks; background workflows executing tasks in processes, and so on. All of these use cases generally are 100X bigger consumers of tokens, and much closer to direct ROI on the spend. This S-1 gives you a sense of both the strength of the frontier lab business model - even amidst massive competition - and how early we are in the build out of AI.

I knew that the Anthropic S1 would have some goofy risk factors but this has to take the cake

how i work with agents in 2026. ignore all other guides on here.

Reaching 3000 Elo takes 20,000+ hours of grinding. Actually, no human has ever done it. So I reverse engineered my Ray-Ban Metas instead. Now a 3000+ Elo AI reads the board and shows me the best move in AR ♟️🕶️ Years of study → 0 seconds.

i figured out how to do this the game is to draw 8 cards and combines them arithmetically to get any 4 digit number consider one puzzle with 1, 4, 5, 6, 8, 9, 11, 12 and target 7217 a solution is 7217=9*(6*(11*12)+5*8/4)-1 first step is to realize that this problem is not only mostly solvable (98% of puzzle has a solution), there are usually many ways to solve it for example, 7217=(12×9-5)×(11×6+4)+8-1 =11×8×(12×6+9+5-4)+1 =11×(12+4)×(9×6-8-5)+1 in fact, most puzzles each has 100+ unique solutions (here a+b*c and c*b+a count as one solution) i know this by writing code to analyze large amount of puzzles, but you can just play the game long enough to notice as well once you build the confidence, here's one practical methodology i found by playing a bit a typical solution consists of the main multiplicative stem and some corrections along the way you can typically get 3-4 out of the largest 5 numbers form the stem, say the numbers are a1, a2, ..., a8 ranked from large to small so final solution typically looks something like a1 * a2 * a3 with some corrections along the way like ((a1 + a5) * a3 + a7) * a8 + a4 + a2 now you do need to be familiar with how to factorize numbers, there are simply tricks like the sum of digits of a number can be divided by 9 then that number can be divided by 9 as well, like 783 can be divided by 9 because 7+8+3=18 can be divided by 9 then you work your work backwards to see what correction is needed on the original target to divide it down you typically already have a rough sense of what the main multiplicative trunk could be take this hand as an example 1, 4, 5, 6, 8, 9, 11, 12 and target 7217 6*8*9*11 = 6*8*99 ~ 4800 which is far from 7217, which means you'll most likely need 11 & 12 in the trunk in fact 6*9*11*12 = 72*99 ~ 7200 looks close! now we work backwards and try to correct it a bit so it can be divided by 9, 11, or 12 here's where there's a bit trial & error but because there're many possible solutions, it tends to work out 7217 digits sum up to 7+2+1+7 = 17, which means with a correction of 1 it's dividable by 9 so (7217+1)/9 = 802 (you need to be able to do this kind of math in your head, which is not very hard but needs practice) now we are left with 4, 5, 6, 8, 11, 12 and we need to make 802 we try to find correction again, for example, what's the correction that makes it dividable by 12? it needs to be dividable by both 3 and 4, it's not hard to tell you can add 2 802 + 2 = 804/12 = 67 once you have that, you know you can make a 66 as well 802 - 10 = 792/12 = 66 once you have 66 we have 6 * 11, then we need to make a 10 from 4, 5, 8, so you can do 5*8/4 the last part might look like luck, but really you can try the divider number exhaustively, and because there are typically many solutions, you'll find a valid solution relatively fast this method almost always works now the deeper reason that there's nearly always a solution is related to percolation theory in math consider a sponge, it's full of holes and water can flow through it. but if the holes are sparser, then at some point it becomes very unlikely for water to flow through there's a "phase transition" at which point the sponge goes from water-permeating to not when it is water-permeating, there's many path for water to flow through in this card game, the number of cards is critical, if you can too few cards, then the probability of finding a right solution decreases dramatically but with enough cards, there's almost certainly a solution, and when there's a solution, there are typically many

The worst fucking thing you can ever do is take a long break from the gym.