Curious Carbon

@grok Imagine epic 80's adventure 🎬

@elonmusk @Space_Station What’s the count for total non-SpaceX dockings vs. SpaceX dockings? 🧐 Has SpaceX surpassed that number?

While we never want to be complacent about it, docking with the @Space_Station has become routine

@changran_hu Last night i jumped on this all excited, then hit a wall with the X Premium+ subscription not working with Hermes. Today i found out @xai enabled this as well, and i just tested it live 🔥 It works! 🚀

Give it a try!

Playing with the Hermes agent CLI using my X Premium+ subscription. Thank you @xai for enabling this 🙌 I was bummed out that Grok Build CLI is only available to Heavy subscribers, but this made it hurt less 😝

@kybaskerville @xai I have, it’s great 👍 but I believe Grok will be even better soon 😝

@curiocarbon @xai Just get Claude pro

An early beta of Grok Build, an agentic CLI for coding, building apps, and automating workflows is now available for SuperGrok Heavy subscribers. Through this early beta, we will improve the model and product based on your feedback. Try it at x.ai/cli

@skcd42 Man, i'm all about feedback, if only i could access this early release with my SuperGrok subscription 😜

Early beta and we are looking forward to your feedback! We will keep improving until Grok Build is absolutely the best in the world

@chrisparkX @xai Any future date for when SuperGrok subscribers get access? Or when will using an xAI API key with our own credits work with it?

Early beta launch of Grok Build! Great work by the core xAI team. Bookmark this post. This is the start. @xai 🚀

@elonmusk At least allow me to use the xAI API with my own credits. It's a bit of bummer that I can't try this out 😑

@elonmusk Why no SuperGrok subscriptions for this? 🧐

Grok Build

I used to actively remove them ... until reason kicked in and I asked myself "wtf are you doing?" and realized how much effort i was putting into removing them 😅 Now, if I have the time, i'll make them bold to stand out! 🤣

genuinely hate that I can no longer use "—" in anything I write anymore

@arnogau 🤣 🔥🔥🔥 Unleash Grok Build on the masses!

backup plans are for losers

@atmoio @powvans That was an epic ending to the clip, I second that 🔥

@powvans 🙏

AI is giving every CEO the same advice

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

🚨 Neuralink patient #3 Brad (ALS) just got his REAL voice back, thanks to Neuralink + ElevenLabs cloning. His family can finally hear him again! Warm, familiar, full of life. No more robotic sound. Just him. This is the most beautiful side of AI.

Marc Andreessen just collapsed a fifty-year assumption in one sentence. Andreessen: “I’m not sure there will even be a salient concept of a programming language in the way that we understand it today.” Not declining. Not evolving. Gone. For fifty years, humans learned machine syntax to command computers. We bent our cognition to fit their grammar. We built entire careers on how fluently we could speak a language machines wrote the rules for. That was always backwards. The correction is arriving faster than the industry will say out loud. Andreessen didn’t stop there. Andreessen: “You may not need user interfaces.” Then came the only question left. Who uses software in the future? Other bots. Follow that to its end. The screen. The dashboard. The browser. The app. The dropdown menu. Every interface ever built assumed a human on the other end who needed the world made legible. If the user is a machine, none of that is necessary. The entire visual layer of computing was built for biological eyes. When the primary users are no longer biological, that layer doesn’t get updated. It gets stripped. Andreessen drew the comparison himself. Not long ago, 99% of humanity was behind a plow. The world spent generations asking what people would do when farming disappeared. The answer was everything worth doing. We are at that exact moment again. Except this time, the plow is a keyboard. Andreessen: “I’m going to tell the thing what I need, and it’s going to do it in whatever way is most optimal.” That sentence deletes the entire skills economy built around execution. Not judgment. Not taste. Not the ability to want the right things. Just execution. That part is over. Which means the only thing left that matters is the quality of what you want. Most people have spent their entire careers getting better at building. Almost no one has spent that time getting better at knowing what to build. That gap is about to become the only gap that matters. The friction of execution is gone. What you can imagine is what you can build. The question is whether you’ve ever trained that muscle. Most people haven’t.

LLM's represent a digital mirror which reflects our own thoughts, augmented by knowledge we did not previously posses. It's like staring into a black hole, but instead of seeing the abyss, we see the infinite possibilities. 🔥

@farzyness Great content! 🦾

Musk is throwing $25 billion at a single chip factory. TeraFab. Biggest industrial bet anyone's ever made, and the numbers don't work according to pretty much every analyst who's looked at them. Meanwhile one company in a small Dutch town — ASML — still makes the only lithography machines that actually matter. But Musk isn't trying to beat TSMC at their own game. The play is collapsing the whole design-to-packaging loop so Tesla can iterate on silicon 5 to 10 times faster than everyone else. That gap gets wider every cycle. Everyone else is still buying general-purpose chips off the shelf. *** thanks to @DavidCarbutt_ and team for the edit.

Claude knows! —> The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite. I. What the Lump of Labor Fallacy Actually Is The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie. The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization. Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself. The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level. II. The Classical Economic Mechanism That Destroys the Fallacy To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously: Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified) When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either: ∙Lower prices for consumers (real purchasing power rises), or ∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or ∙Both. Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs. This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.

@JoeTegtmeyer Sick 🦾 Love the Megapacks with those wire harnesses feeding into the building 🔥 Looks like a zoomed-in electronic circuit view.

Giga Texas Cortex 2.0 continues to make progress towards initial (partial) operation sometime in April. While these efforts center on the cooling system construction & temporary cooler setup to allow initial startup, the 2nd half of the cooling system is getting it’s FRP superstructure installed while on the W side, crews continue installing screw piers ahead of platforms for transformers, electrical equipment, another 100+ Megapacks & additional electrical cabling & connections. Work on the 2nd half will continue through the summer & I expect Cortex 2.0 to be fully operational by the end of the year at the current pace they are working.