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⚡️The early Bitcoin holder may be one of the cleanest wealth outcomes capitalism can produce. Think about what happened to the rare person who accumulated meaningful Bitcoin very early and actually held it. They did not need to build a 10,000-person company. They did not need customers. They did not need employees. They did not need a board. They did not need to manage LP money. They did not need to continually recreate the economic engine that made them wealthy. They made one extraordinarily consequential capital-allocation decision, survived years of uncertainty, and eventually the asset itself did almost all of the scaling. That is an incredible lifestyle outcome. A founder worth $100M may technically have the same net worth, but much of it can be trapped inside a company requiring their attention, carrying payroll, lawsuits, competition, customers, executives, financing, and constant strategic decisions. The early Bitcoin holder can have $100M sitting in an asset that requires almost no organizational complexity to own. Enormous wealth. Tiny operating surface area. That combination is rare. There is also something profound about how the wealth was created. They recognized a new category before the world agreed the category was real. The wealth came from carrying uncertainty. Bitcoin looked ridiculous. Then dangerous. Then speculative. Then legitimate. Then institutional. Every step upward removed uncertainty while also removing asymmetry. The earliest holders were compensated for occupying the period when the world could still plausibly say the entire thing was worthless. But survivorship bias here is enormous. Thousands of people also concentrated into things they believed were revolutionary and lost everything. Even among people who bought Bitcoin early, many sold at 2x, 5x, 10x, or 50x. Others lost keys. Others got destroyed by exchanges. Others overleveraged. Others simply could not psychologically carry an asset through repeated 70% to 90% drawdowns while everyone around them told them they were insane. So the extraordinary outcome required several things to happen together: see correctly very early, size enough for being right to matter, survive enormous volatility, preserve custody, and resist selling when the gain already looked life-changing. That is almost impossibly difficult in real time. The highest form of wealth creation may actually be finding a very small number of generational capital-allocation decisions rather than continually maximizing labor output. One Bitcoin-like decision can compress decades of economic effort into ownership of the right thing at the right moment. Afterward, the entire purpose of wealth can change. You no longer need to optimize for more work. You can optimize for autonomy. Time. Privacy. Research. Relationships. Travel. Building things because they matter to you rather than because they need to pay you. And compared with becoming a billionaire CEO whose phone can never truly be turned off, the anonymous person who quietly owns enormous permanent capital may have won a different and arguably more valuable game. The founder builds a machine powerful enough to create freedom later. The generational investor finds an asset powerful enough to make the machine unnecessary. That second outcome is incredibly rare. But when it happens, the lifestyle asymmetry is almost absurd.

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SCOOP: Delta Airlines employee training is FILLED with racist DEI courses They include mandatory courses on "unconscious bias" and aim to turn employees into "Advocates for DEI." Hopefully @AAGDhillon can investigate if @delta has racist practices!

American says she was called for jury duty and didn’t want to spend multiple days wasting her time sitting in a jury So she found a way to get out of jury duty When she was interviewed they asked “Do you have any bias against any types of other people?" She says yeah, she’s racist. They asked to elaborate: “I was like, "Yeah, I'm racist towards every type of race. It doesn't matter what you are. I hate all races." Needless to say, I never got called for jury duty ever again.” This hack actually works because you are expected to be fair and impartial to who’s being tried for a crime But if you use this, it has to be true or you can face penalties Jury questionnaires and oral answers are given under penalty of perjury. You can’t lie under oath

Grok 4.6 is my daily driver now. This is why: When I’m using interactive agents in a CLI/terminal (or even Slack), what matters most to me is speed & token throughput. Intelligence and differences in performance on benchmarks is negligible to me when speed is sacrificed. If I am there to steer & guide the agent, or make some quick fixes, then I don’t want to wait 5+ min per turn. Opus 5 (fast) is a good solution to this, but it is WAY too expensive. Grok 4.6 is perfect though, incredibly fast, almost as smart as Fable, and relatively much cheaper than anything else right now. On the flip side, for cloud agents working in Factories & background tasks triggered on crons, I don’t mind using intelligent, slow models like Fable / Sol. I’d even opt for a model router for cloud tasks that biases to heavy, smart models. I guess the meta point I’m making here is that model choice is largely a function of whether there’s a human waiting in the loop.

i dont think there's a right or wrong answer. i just think it's fucked that people get enraged at the thought of someone choosing their own companion animal over a stranger human. as if it's the inherent truth that non human animals have less value than humans, which is merely a cognitive bias

I used Grok 4.6 to build a 2D agent simulation of The Office. Here are notes and observations on 4.6: It is a huge step up from 4.5 - both in the quality of visual outputs and in the breadth of work: personality, features, and finding usable assets for this project. The editorial content and writing were good - I like the "feel" of this model. One of the biggest wins for 4.6 is speed. The throughput (~80 tok/s) and latency are so good that it feels Composer-adjacent - something @ericzakariasson called out in his writeup. That speed + cost + performance makes for a true "workhorse" model that's practical for both personal and work use. My goal was to simulate The Office through "agents" that behaved like the characters - including all of the neuroticism and idiosyncrasies that made the show so funny. I knew what I wanted artistically - I've built similar 2D apps with pixel art. I asked Grok 4.6 to research psychological modeling, pull character descriptions, find references for the cast, and assemble 2D sprites with the Universal LPC sprite sheet generator and some RPG tilesets I grabbed online. I rattled a brief into the @cursor_ai mobile app late one night at the Cursor office and woke up to an MVP. Then I spent a day or so spamming cloud agents until the simulation worked how I liked. The cool part is that Grok did the simulation, art, and research on its own: - Every couple of minutes, each person scores a few possible actions and picks one. They pathfind around desks, work, grab coffee, talk at the cooler, hide in the annex, or end up in a meeting. - Click someone and you can inspect traits, needs, mood, relationships, and whatever they're doing right now. You can also speed time up, force Michael to call a meeting, or roll a random office event. - Each character has Big Five scores that bias what they tend to do. Needs like work, social, caffeine, status, and comfort rise over time and fall when they do something about them. - Grok reconstructed the layout from Dunderpedia (Office Wiki) and online sources - Michael's office and the conference room on the north glass, Pam at reception, sales and accounting in the bullpen, the annex to the east, restrooms and the warehouse down the stairs. - Grok helped me find a few sprite sheet packs to implement. I was surprised at how accurate some of the character sprites were - from hair color to clothing. The combination of a performant model, cloud workspaces, and high throughput made for a really fun devex - the amazing folks at @cursor_ai and @SpaceXAI make my job easier everyday. A huge shout-out to @_Brian_Zhang, whose Notion hackathon project inspired this exploration.