What is the most capable AI system that can run entirely on hardware you own, understand, and control? Singularity Machines created and validated a 30-command Bash hardware-discovery framework to transform Silicon from a black box into a systematically mapped system—revealing its compute, memory, security, AI, media, thermal, and communication subsystems—while demonstrating how the power of a 16-core Apple M3 Max, 40-core GPU, 48 GB of unified LPDDR5 memory, dedicated Neural Engine, Video Engine, Secure Enclave, and high-speed memory fabric can support increasingly capable offline AI models and autonomous local-first systems operating entirely on hardware that is owned, understood, and controlled by the user. For Singularity Machines the objective is not to chase the largest possible models, but to discover the upper limits of intelligence, autonomy, and utility achievable within the physical constraints of a machine we fully own and understand. Try the framework yourself, map your own machine from silicon to software, and discover what your owned hardware is truly capable of before letting someone else define its limits. #SpeedySoftwareServicesAnyTechStack #AIAgenticAutomation #AIoperatedSovereignPrivateLocalOfflineSystems

The core missions of Elon’s companies are always about maximizing the agency of humans. If you have trouble driving, the car takes you where you want to go safely. If you need care, Optimus will be there. If you want to avoid traffic, there is Boring. If you need connectivity, Starlink provides it. If you want to go to space, Starship lowers the barrier to entry. If you are paralyzed, Neuralink restores your agency. If you want to be heard, X is there for your voice. If you want to build something, Grok Build realizes your ideas. By doing so, it inspires others to build. I am inspired to create a world where my kid has better access to agency than I do.

My college degree recommendations for fresh high school graduates in the age of AI to be prepared for the next frontier: - Applied Physics (math and physics) - Applied Materials (physical agentic) - Agriculture (food production in space) - Aerospace (frontier transportation) - Civil Engineering (infra life support from Earth and beyond) - Electronic Engineering (scaling compute and communications to the solar system) - Manufacturing (where things get made) - Mechanical Engineering (packaging, manufacturability, tolerances and cycling) - Medicine (personal drugs in space)

There are nothing more powerful in the universe(s) than human 🧠s and 🫀s they got us to this point not the other way around

A clear decision is a clean translation between what you know and what you do. #SingularityMachines #DecisionTranslationHarness

How can the @X algorithm be explored to make something very interesting? github.com/xai-org/x-algorit…

we are not too bad on the smaller frontiers: LFMs @liquidai

LiquidAI has built a tiny reasoning model that achieves useful performance for on device use cases with only 1B active parameters liquid.ai/blog/lfm2-5-8b-a1b

I'd recommend trying out @liquidai local LFMs for yourself & designing your own personal benchmarks that serve your specific purpose rather than rely on evaluations that fall outside of the scope of what you are specifically doing. I will be taking a look at their offline performance in building full-stack production level applications only with the tools already available on my device. This includes moves like using the terminal & bash for the entire development process & using Assembly Language for the core build. I'm not necessarily looking for agentic automation yet, but rather its ability to work alongside an engineer to create something deployable to the real world using a step by step collaborative approach. With bash, you don't need an "IDE" because the commands themselves have the power to write, change & review code which could be beneficial to a model as you dive lower into the control. AI is helping us to leverage these powerful native tools once again and is putting them back at the center of the development process in AI-assisted workflows. Plus, as an open-source provider and having witnessed the Liquid AI launch event at @MIT gotta support the smaller local groups going against the mammoths holding up the status quo. 🚀 huggingface.co/collections/L…

The answer is not overregulation that helps incumbents, it's to densify and decentralize compute. Everyone should own their own personal AI supercomputer that is an extension of themselves.

When I pay $300 for @grok Super Heavy to use Grok Build I expect to be as lazy as I can in the development process totally hands-off for a fee like that. We are in pursuit of this simple 3D landscape called Elyndor, invented & built by Grok, & when prompted to code-in Elyndor Castle in the shape of a dragon, it drops in this massive black blob that destroys half of Elyndor even after a bunch of prompt massaging. I have already used 5% of my inference allowance for this cycle which is kind of nuts. This is a thirty day lazy dev & evaluation of Grok Build please follow along before we cancel the subscription at the end of the month! @elonmusk @skcd42

SITUATION DETECTED: Google DeepMind’s AI agent autonomously solved 9 of 353 open Erdos problems in mathematics, at a cost of a few hundred dollars per problem.

Pursuit of happiness, builder edition: - Find a problem you love spending your life solving. - Spend your life solving it.

A human Rorschach test is a metaphor drawn from the classic inkblot psychological assessment. It describes any ambiguous stimulus—art, a statement, an event, or even an AI response—where people project their own subconscious biases, fears, hopes, and personality traits onto it. The "test" reveals more about the interpreter than the thing itself. In short: what you see in the blur says everything about you. What's prompting the question?