Two years ago, we embarked on a journey to democratize and preserve human intellect through our DigitalMe project. While AI technology has evolved rapidly since then, our vision remains as relevant as ever. This throwback showcases our early implementation using D-ID, ElevenLabs,

Google just announced Private AI Compute, running Gemini in a sealed cloud enclave so your data stays private to you while still getting big-model reasoning. Think Apple’s PCC, but on Google’s TPUs with Titanium Intelligence Enclaves (TIE), remote attestation, and end-to-end

A big unlock for robot learning just dropped. This is the kind of scale that moves imitation learning, VLA agents, and skill transfer out of the lab and into real work. Why it matters (our take): • From SOPs to policies: turn expert workflows into trainable action policies

A tiny Stanford startup just took a swing at personal robotics. Innate’s MARS is a $2K, open(ish) dev platform with a 6-DoF arm, 2D LiDAR, Jetson Orin Nano, and crucially, an onboard AI conductor called BASIC that can see (VLMs/SAM), reason (LLMs), remember (spatial memory), and

☀️🚀 Google's Project Suncatcher is wild: Solar-powered TPU constellations in orbit for unlimited AI compute; 8× sun power, vacuum cooling, terabit laser links, launching prototypes 2027! Trillium chips survive 15× space radiation. The future of scalable ML isn't on Earth, it's

Edison Scientific just launched Kosmos, an AI Scientist with a structured, persistent world model that can read ~1,500 papers and write ~42,000 lines of code per run, with ~79% reproducibility and 7 discoveries reported (neuroscience → materials → clinical genetics). Not a

🤖 GEN-0 from Generalist AI is here: A 10B+ model trained on 270K+ hours of real-world robot data, unlocking seamless think-act harmony and epic scaling for dexterous tasks like packing unseen gadgets. Phase transition at 7B params means bigger bots evolve without breaking! The

🚀 China's quantum wizards just 'teleported' data across epic distances via fiber networks, unlocking unhackable global comms! Is the quantum internet here? Watch to unpack the spooky science. Dive deeper in this fresh Nature paper: https://t.co/i3zPcDBlxw What quantum tech

AWS just activated Project Rainier, nearly 500,000 Trainium2 chips online, with Anthropic already training/serving Claude and plans to scale past 1M Trainium2 by end of 2025. Why it matters (our observer take): • Throughput at frontier scale. UltraServer + UltraCluster

Thermo-computing just stepped out of the lab. ⚡️ Extropic’s thermodynamic sampling units (TSUs) flip the stack: instead of crunching matrices on GPUs, they sample probability distributions in hardware; what generative AI needs at inference time. Why it’s interesting: 🔹 Energy

The Blueprint for Speed: xAI’s Colossus in 122 Days Infrastructure, not algorithms, is now the competitive edge. xAI repurposed a 785k-sq-ft Electrolux factory in South Memphis and stood up the world’s largest AI supercomputer in ~4 months; scaling from 100k → 200k NVIDIA GPUs

The future is here, but it needs your help. 1X Technologies has made science fiction a commercial reality, opening pre-orders for the NEO humanoid robot ($20,000 one-time / $499 per month), and the path to useful autonomy is intentionally human-in-the-loop. 🔹 What’s new:

DeepMind just marked a milestone: RL algorithms discovered by AI rather than hand-designed. Their Nature paper shows a meta-learning system that trains across many environments and emerges with an update rule that outperforms SOTA baselines (incl. Atari) and generalizes to

Seed3D 1.0 (ByteDance) lands a direct hit on the content bottleneck for robotics and simulation: high-fidelity, simulation-ready 3D assets from a single image; geometry, UVs, and PBR materials that drop straight into physics engines. Why we’re watching: • Faster sim turnarounds

Across this four-part series, we have systematically covered all the core architectural pillars of the AI data center. Now, we solve the only thing that matters: The Latency Constraint. Training is 10% of the cost; Inference is 90% of the operation. The architecture shifts from

Google Quantum AI has crossed an important line: verifiable quantum advantage with Quantum Echoes, an algorithm that computes an out-of-time-ordered correlator (OTOC) and, on the Willow processor (105 qubits), reportedly runs ~13,000× faster than a leading supercomputer. What’s

Space isn’t just for telescopes anymore. Starcloud plans to put GPU clusters in orbit. Their November launch will fly an NVIDIA H100 to space, demoing in-orbit AI that taps 24/7 solar and uses the vacuum as a heat sink (no evaporative cooling, no water). The long game:

DeepSeek just reframed long-context: compress the document into vision tokens instead of feeding raw text. Their new DeepSeek-OCR turns pages into ~100 vision tokens and still reports ≈97% decoding precision at <10× compression (and useful signal even at 20×). It also

Direct Drive Tech just launched D1, a fully modular, all-domain splicing robot that can reconfigure wheel-leg + leg-leg + wheel-leg→leg for the task at hand. Think field robot that assembles/splits on demand, trades stability for agility when needed, and is built for swarm

We all focused on GPU cores. The real battleground for massive AI is Data Logistics. In the third installment of our series on the AI Factory, we dissect the architecture that solves the Straggler Problem, where one slow component can stall an entire supercluster. Dive into the

RTFM is a notable step toward interactive world models: a single H100 generating 3D-consistent frames in real time without building an explicit mesh. Think learned renderer that infers geometry, lighting, shadows, and reflections directly from video-scale training, and can even