Jason PH

AI is trapped in bits. It's time to set it free into atoms. Last week @karpathy open-sourced autoresearch. It ran 126 ML experiments overnight and found optimizations he'd missed in 20 years. Most people saw "AI is replacing researchers." I saw something else: the boundary of AI's current world. AI's superpower isn't intelligence. It's relentless trial and error. Give it a clear loss function and instant feedback, it'll try ten thousand things overnight. In code and math, this is devastating. No human can compete with a system that never sleeps, never gets bored, and runs experiments at the speed of electrons. But SpaceX — the fastest hardware iterators in human history — still took ten years to get Starship right. Each launch takes months to prepare. You can't blow up 126 rockets in one night. The physical world simply won't give AI the fast feedback loop it needs. Today, AI is like a genius locked in a library. It can read every book ever written, but it can't step outside and touch the grass. This isn't a limitation to fear. It's a frontier to build toward. Software has been optimized for decades. But manufacturing, energy, materials, biology? Century-old processes that have never seen a million experiments. The inefficiency in the physical world dwarfs anything left in the digital world. The real gains — the 100x gains — are hiding in atoms, not bits. The question is: how do you give AI a fast feedback loop in the physical world? Three things need to exist. First, streams of real-world physical data — from sensors, cameras, devices, machines — flowing continuously into AI systems. Not static datasets scraped from the internet, but live signals from the world itself. Second, verifiable computation — so AI's conclusions about the physical world can be trusted and reproduced, not hallucinated. Cryptographic proofs, not vibes. Third, a decentralized workforce — machines and people that can execute AI's hypotheses in the real world, run the physical experiments, and close the feedback loop. Data from the world. Verified by math. Executed by a swarm of agents. This is what we're building at IoTeX. Not because we want AI to be dangerous, but because we believe AI's true potential is wasted if it stays trapped in bits. The physical world is where the real problems are — climate, energy, manufacturing, health — and solving them requires AI that can iterate on reality, not just on text. Autoresearch proved that AI's iteration speed is essentially unlimited when feedback is fast. The unlock isn't making AI smarter. It's making the physical world legible and responsive to AI. Whoever builds that bridge — from bits to atoms, from tokens to reality — defines the next era. We're building that “bridge”. Open, verifiable, decentralized. Not because it's trendy, but because when AI finally learns to experiment on reality at the speed it experiments on code, the stakes are too high for that loop to be closed and opaque.

아이오텍스(IOTX) 거래 유의 종목 지정 해제 아이오텍스(IOTX)의 거래 유의 종목 지정 해제되었습니다. Investment warning period for IOTX has been lifted. 🔗 Discover more: upbit.com/service_center…

came across ioSwarm recently, it lets you run an AI agent node on the IoTeX network that automatically validates transactions and earns you $IOTX decided to try it myself. just dropped the ioswarm GitHub link (github.com/iotexproject/i…) into OpenClaw and asked it to handle the setup and get it running on its own. was surprised how easy it was, it started working with just 1 instruction left it overnight and and I've already started receiving payouts. i'm earning around 150-200 iotx/day at the current reward rate. the ioSwarm agent basically connects to the goodwillclaw delegate, validates IOTX transactions and sends rewards directly to your wallet pretty cool 🔥

아이오텍스(IOTX) 거래 유의 종목 지정 해제 아이오텍스(IOTX)의 거래 유의 종목 지정 해제되었습니다. Investment warning period for IOTX has been lifted. 🔗 Discover more: upbit.com/service_center…

Huge thanks to everyone who joined us for the #DCBlockchain Summit. You showed up, engaged, and made this event what it was. The conversations throughout these two days were exactly what this industry needed right now. Until next time 🙌

🚀 This might be the fastest local LLM inference engine on Mac — open source. Rapid-MLX is built specifically for Apple Silicon. Tested across 18 models vs Ollama, mlx-lm, llama.cpp — fastest on 16 of them. ⚡ What makes it different: • DeltaNet state snapshots — multi-turn TTFT drops from 1.5s → under 200ms • 100% tool calling accuracy (function calling actually works) • OpenAI-compatible API — drop-in for Claude Code, Cursor, etc. 🏆 Qwen3.5 is where it really shines: The hybrid RNN+attention architecture needs special handling. Other engines re-compute full context every turn. Rapid-MLX snapshots the RNN state and restores in ~0.1ms. 📊 Numbers (Mac Studio M3 Ultra, 256GB): • 397B — runs on a single Mac. 209GB. No cloud. • 122B → 57 tok/s, 100% tools • Coder-Next 80B → 74 tok/s, 0.10s TTFT • 35B → 83 tok/s • 9B → 108 tok/s (2.3× faster than Ollama) Fully open source: 🔗 github.com/raullenchai/Ra… @awnihannun @reach_vb @simonw @JustinLin610 @exaboross

The DC Blockchain Summit is officially underway! Join us as industry leaders and innovators shape the future of blockchain and digital assets. Stay tuned for live updates! x.com/i/broadcasts/1… #DCBlockchain