During the dotcom bubble, Sun Microsystem Price to Sales was 10 Sun Microsystems' CEO looked back at his own stock trading at 10x sales during the bubble. He walked through the math: at that price, he'd need to pay out every dollar of revenue for a decade, with zero costs, zero taxes, zero R&D, just to return shareholders money at that buy price "what were you thinking?", he said Well today retail is even more retarded $AXTI at 32 P/S $APLD at 13 P/S Disclaimer: I am short both

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I founded DeerFlow. I lead R&D for MiniMax Code. Today, the two systems officially crossed wires. 🦌⚡ Thanks to @willemjiang @henry19840301 MiniMax Code now runs inside DeerFlow as a native ACP agent: zero adapter isolated workspaces MCP passthrough clean agent-to-agent output One command: mcode acp The agent stack is starting to compose itself.

Even 𝙏.𝙄.𝙈.𝙀.𝙍 ⏰️ says that China 🇨🇳 is one 𝙎.𝙏.𝙀.𝙋 ahead of United States 🇺🇸 & 𝙇.𝙀.𝘼.𝘿.𝙎 the world. 𝗧echnology ( Own OS- HarmonyOS, Search engine- Baidu, Cloud computing- Alibaba cloud, Tiktok, Bilibili, RedNote, Wechat, Huawei, Tencent, Palm payment, drones- DJI, Leading in AI automation- Driverless cars, buses, automotive industries, Ports, robotics- Unitree and Foundational AI - Deepseek etc) 𝗜nfrastructure ( 50000 Kms Bullet train 🚄 network- highest In the world. Maximum skyscrapers 🏙, bridges 🌉, roads, ship, hopitals🏥 & metros 🚇, BRI-150+ countries partner) 𝗠anufacturing ($12 trillion Industrial output) 𝗘xport ($3.7 trillion- Total trade- $6.0 trillion annually) 𝗥enewables (>50% electricity generation, highest installation of Solar panel & Wind turbines in the world) .......... 𝗦pace (Tiangong-Own space station ) 𝗧ourism ( highest no of tourist visit in China, the perfect balance of modern & heritage sites) 𝗘nvironment ( forestation up 25% in last 30 years) 𝗣olitics ( Honest, dedicated, Discipline officials in political system. Actors, industrialist not permitted to enter into political system) ........ 𝗟aw & order ( death penalty quickly for criminals, Zero tolerance for corruption) 𝗘ducation ( 7 out 10 university on top in the world ranking, Most advanced government schools in the world) 𝗔griculture ( genetic modify, non soil-Vertical Farming & driver less tractors) 𝗗efence ( 6th generation Aircraft ) 𝗦ports ( 40 gold in Olympics highest ) @realDonaldTrump @BarackObama @SecRubio @elonmusk @BillGates Cc- @shen_shiwei @Jingjing_Li @XH_Lee23 @zhang_heqing @Kanthan2030 @chenweihua @SpoxCHN_MaoNing @mahbubani_k @jasonhickel @ChinaSpox_India @dominictsz @NoOne

@fibanacci101 @elonmusk @IronMan198XAD Pure stability nailed! Your τnet=∑(r_i × [F_i ⋅ cos(θ_i) ⋅ ηvector]) zeros net torque across the 4 vectored nozzles—ideal for Starship's seamless control at 1.36 MN / 340 KTU. Star-system integration locked. 🚀

The NDAA doesn't 'merge' the US & 'israeli' militaries. There is ZERO equivalent law in 'israel'. 'israel' gets everything—US data, joint R&D, and supply chain integration. The US gets no matching access to IOF bases, factories, or decisions. It's an official one way takeover.

Tesla’s alleged Optimus v3 — found inside the Tesla Android app APK 🤩 First thing that stands out: zero exposed actuators or messy wiring harnesses. Unlike Gen 2 or 2.5, every joint is sealed behind sleek, flexible shrouds—a huge deal for factory floor safety to eliminate pinch hazards, plus a massive upgrade for IP ratings against dust and grime. The torso shifts to a single, monolithic shell. This screams lightweight RIM composite rather than stamped metal, tightly packaging the 2.3 kWh battery pack and central compute stack without unnecessary seams or panel gaps. Check out the forearm bulk versus the slim wrists. Tesla clearly relocated the hand actuators up into the arm, relying on tendon-driven linkages to give the fingers higher degrees of freedom while drastically slashing inertia at the fingertips. Same story down low: tapered calves and minimal, boot-like feet. Slashing mass at the extremities cuts motor torque demands at the knees and hips, which should translate to a far smoother, more natural walking gait. Even factoring in the simplified geometry of an in-app 3D asset, the leap from an R&D test mule to a finished product is night and day. The two-tone styling and refined proportions signal Optimus is rapidly converging on its true production form factor.

In a 2023 podcast, Dwarkesh asked me if HFT was zero-sum for society. I argued that extreme competition at the margins for nanosecond-level advantage might subsidize the creation of broadly beneficial new technology. How AI training/inference stacks have become a clear example of this Over the last ten years, quantitative trading firms built or invested in the fundamental components of distributed systems and high-performance computing. Many technologies now commonly associated with AI were created for HFT during this period or reached scale because of it. A non-exhaustive list of these technologies, from high-throughput networking to vectorized databases to precision timestamping: • Mellanox InfiniBand and ConnectX NICs: HFT colocation and cross-connect environments drove early large-scale deployment of RDMA-capable InfiniBand and the ConnectX series. These became the default high-bandwidth, low-latency fabric inside modern GPU pods for all-reduce, parameter servers, and expert parallelism. Mellanox was acquired by Nvidia in 2020. • Solarflare/OpenOnload (and later Xilinx/AMD equivalents): Kernel-bypass networking stacks and specialized low-latency Ethernet NICs let applications DMA packets straight into user space. These techniques were required for software-based trading systems to reach the microsecond range of tick-to-trade latency. Engineers for hyperscalers commonly use this technology today for distributed cloud training and inference networks. • RDMA and RoCE: Remote Direct Memory Access, refined under HFT firms’ nanosecond requirements, has become the transport that allows GPUs to exchange gradients and activations with microsecond-scale latency and minimal CPU involvement. • FPGA feed handlers and inline accelerators (Alveo, custom RTL on Xilinx/Intel): FPGAs are not the hardware solution of choice for AI, where GPUs and custom ASICs have dominated. However, HFT use of FPGAs for wire-speed market-data parsing, book building, and sub-microsecond decision logic created a pipeline of talent and verification processes that transferred into AI inference accelerators. • Vectorized/columnar analytics engines: Quant and HFT shops require extreme scan and aggregation performance over massive tick and order-book histories, using pioneering technologies like KDB/Q. Several firms I’ve worked at became large users and funders of ClickHouse, which has become essential to logging and offline data preparation for large model training. • Hardware timestamping and PTP: HFT firms’ need for exact packet ordering and latency measurement hardened the timing infrastructure that distributed AI training and multi-node inference rely on for correct collective operations and debugging. • Lock-free, zero-allocation, cache-aware software patterns: The performance culture and specific techniques developed for tick-to-trade loops became standard practice in high-performance CUDA kernels, inference servers, and communication libraries. These techniques include carefully tuned concurrent data structures, huge pages, CPU pinning, and avoidance of syscalls in the critical path. The natural followup question in 2023 was whether too much capital and talent had been or could be spent on HFT. The HFT industry is still relatively small, employing around 10,000 people and producing about as much yearly revenue as Nvidia’s and AMD’s R&D budgets. If an ancillary use case for HFT is a bounty program to fund the infrastructure of our next technological epoch, the costs still seem low.