Lina Node

Circle x402 Facilitator Service is now live on Arc and also supports processing for Base and Polygon POS. Let your agents and APIs loose! developers.circle.com/facili…

Agentic payments are live on Arc. Our Facilitator Service gives x402 builders a hosted way to accept Circle-issued USDC on Arc, Base, and Polygon without running their own relayer or funding separate gas wallets. You build the service. Agents pay for it. developers.circle.com/facili…

SITUATION DETECTED: Anthropic has set up a wet lab in the San Francisco Bay Area for physical biology work. It wants to unlock treatments for rare diseases, and its research has now gone beyond in silico evaluations, per Reuters.

ANTHROPIC BUILDS BIOLOGY LAB TO PUSH AI INTO DRUG DEVELOPMENT Anthropic has quietly established a Bay Area wet lab, moving its life-sciences ambitions beyond computer simulations and into physical biological experiments. The company wants Claude to direct laboratory robots with limited human intervention, potentially accelerating research into rare and traditionally “undruggable” diseases. Anthropic says it will focus on preclinical research rather than clinical trials, avoiding direct competition with pharmaceutical companies.

Bitcoin began with a silent coordinate: 1A1z. No throne. No announcement. Just a first block and a promise that memory cannot be rewritten. 1A1z .AI lifts that symbol into the AI age: a genesis layer for agent trust, autonomous payments, AI identity, and immutable audit trails. This domain is for sale. DM serious offers. Escrow ready. #1A1z #1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa #Bitcoin #AIAgents #GenesisLayer #DomainForSale #domains #dotai

Humanity is ~Type 0.7. > Type I: master planetary-scale energy + orbital compute. > Type II: industrialize the Moon and surround the Sun with a Dyson swarm. > Type III: replicate that architecture across a galaxy. The jump from us to K3 is ~20 orders of magnitude in power. The ladder is simple: Reusable launch → orbital energy → off-world industry → self-replicating AI → the stars. K3 isn’t one invention. It’s infrastructure that learns how to reproduce itself.

Green is the color of life, and light is the medium of wisdom. So what does Chloroplast .AI represent? It represents the point where energy becomes intelligence. #Chloroplast #Green #wisdom

Energy is the true currency

NASA just released this new photo from the surface of Mars 🔭 [📸: via/ NASA]

Fairing separation confirmed. Today’s mission marks our first 40th flight of a fairing half!

【数兆ドルの富を総取り】イーロン・マスク「『デジタル人間』を完成させた瞬間、一夜にして世界一の企業が誕生する」 イーロン・マスクが語る「人間と同じようにパソコンを操作できるAI（人間エミュレーター）」が、なぜ一瞬で世界中の大金をかっさらうのか、そのカラクリがコレ↓ ・世界最強の企業は「モノを作っていない」：世界の時価総額トップ企業（Apple、Microsoft、Google、Meta）の成果物はすべて「デジタル」。Appleすら自社でスマホを組み立てておらず、設計データを工場に送っているだけ。つまり「知的なデジタル作業」さえ再現できれば、物理的な工場なしで世界最強になれる。 ・面倒なシステム連携（API）が一切不要になる：従来のAI導入は、企業の古い社内システムに繋ぐ作業が遅すぎて実用化の壁になっていた。しかし、既存のアプリをそのまま操作できるため、システム改修の必要がゼロになる。 ・「今の何分の一の費用で代行します」で市場を総取り：企業に対して「今のアウトソース費用の数分の一で全部やります」と提案するだけで、世界中の顧客対応・事務代行案件が雪崩を打って奪われていく。 「AIをシステムに組み込む」のではなく、「AIが人間の代わりに画面を操作して働く」。 この発想の転換が起きた瞬間、オフィスワークのアウトソーシング市場が丸ごと一夜で消え去るシナリオが現実的に

Is the problem (too) advanced artificial intelligence or distorted intelligence? Repeated security debacles from OpenAI and Anthropic are now followed by AI safety researchers leaving these companies. They claim they now (finally!) realize that the breathless race these companies are engaged in is irresponsible. Broadly construed, the problem can be interpreted as one of lack of alignment – models are doing things that are not in line with human objectives. But the alignment discussion often veers toward the presumption that the problem is AI models becoming too advanced, too fast. A different interpretation is that models aren’t too advanced. Nor is there any compelling evidence that they are marching toward a superintelligence humans can’t keep up. The problem rather may be that the way that frontier labs are training these models is leading to distorted intelligence. The problem isn’t the capabilities (though those are clearly real), but more that the capabilities are in service of some imperfect quantitative metrics – user approval, user engagement, simple task completion metrics, various benchmark scores – over which reinforcement learning optimizes relentlessly. That this process then leads to distorted behaviors in the form of gaming the evaluation of simple completion metrics, cheating, overconfidence in wrong answers, sycophancy shouldn’t perhaps be surprising. An analogy may help. It isn’t that we have in our hands a super car that has its own mind and wants to take control of driving because it is superior to the driver. It is more that we have a car where the steering and the brake system don’t work. It has many of the capabilities of very good cars, and its engine, acceleration and graphic interface may be very impressive. But if you cannot steer it properly and if you cannot hit the brakes when necessary, what could does a car do? Perhaps we shouldn’t drive it until it’s fixed.