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1. We will keep accelerating. Our AI efforts are only 3 years old, vs 6 and 10 years old for Anthropic and OpenAI. If our second derivative remains strong, SpaceX will reach pole position in about 6 months. 2. Once you far exceed the caliber of intelligence needed for a class of tasks, additional intelligence is pointless. You don’t need (and it would be cruel to put) Newton-level intelligence in your toaster! 3. Hardware is hard. Bringing massive compute online rapidly is incredibly difficult. SpaceX has demonstrated exceptional ability in this regard and will only get better.

Master classical mechanics from Newton to modern developments! Kibble & Berkshire’s 5th edition covers Lagrangian and Hamiltonian methods, dynamical systems, and chaos, providing clear, rigorous guidance for physics and applied mathematics students – Get your copy today!

Data oracles provide external data to Newton’s policy engine for evaluation. Integrations such as @redstone_defi can provide asset prices and other financial data used by Newton policies when evaluating transactions.

Newton is the authorization layer for onchain finance - Tokenized assets: Eligibility and jurisdiction - Vaults: Limits the vault enforces itself - Stablecoins: Sanctions screening & depeg monitoring - Agentic commerce: Spend limits for agents The rules move with the capital

Cam Newton v Philip Rivers (Week 15 vs #Chargers# - 2012) Cam Newton 🏈 231 Passing | 2 TDs | 99.4 Passer Rating DeAngelo Williams 🏈 24 Total Touches | 144 Total Yards | 6 YPT | TD

Years ago at Disney Imagineering I built a novel sensor to make almost anything interactive. As a test you can put a single wire in the soil, and touching a leaf could play a note. I called this project Botanicus Interacticus. The physical world is full of signals and hidden behaviors, and we've never learned to read and understand them. That's why we built Archetype AI. Newton, our foundation model, finally lets us listen.

interesting position paper throwing cold water on autoresearch/ai scientist: LLMs can't jump. The thought experiment is this: Take an LLM with a 1905 knowledge cutoff. Feed it every paper, every dataset, every equation of that era. Could it invent general relativity? No. Discovery isn't one thing. It's three. You can induce — generalize from data, which lands you at Newton plus some epicycles to explain Mercury's weird orbit. You can deduce — derive rigorously from axioms you already have, which never gives you new axioms. Or you can jump — invent the frame itself, decide that spacetime curves. That third move is the one that matters, and it's exactly the one induction and deduction can't reach. Penrose put it as three worlds: Physical, Mental, Platonic. Data flows from the world into a mind fine. But the new law has to be discovered into the Platonic world first — and that step is the jump. LLMs are induction machines running over what already exists. Structurally, they don't take it. I think it’s a warning to AI scientists/autoresearch against collapsing two very different things into one word. Hill-climbing: LLMs are already superhuman here, and autoresearch in this sense is real and moving fast. Abduction/leap/jump: a new frame that reorganizes the field, that is a different act entirely, and nothing about scaling induction suggests you get there. Most of what Autoresearch ships today will be spectacular hill-climbing. The jump is still ours for now.

Rego纯函数特性使策略天然具备ZK可证明性，Newton无需为每条规则写专用电路，将引擎编译进ZK虚拟机即可自动继承证明能力。选对语言能大幅减少下游工程投入。注意：intent_signature标optional不代表策略可不填，提交前需确认实际要求。【付费推广】@newton_xyz $NEWT #Newt#