30 years later: are we living Amazon’s own Barnes & Noble moment? 1996: Len Riggio takes Bezos to dinner, tells him B&N's website will crush Amazon, then floats a partnership. Bezos passes 2026: Amazon passes on Muse. Bets its own agent owns the next interface Interesting.

$META’S MUSE IS OUTPACING CHATGPT’S EARLY MOBILE LAUNCH ON SEVERAL METRICS Muse reached 2.8M downloads in its first 12 days, across both app stores. On a more comparable U.S. + Canada iOS basis: • Muse: 1.8M downloads in first 12 days • ChatGPT: 1.3M Muse also set a new U.S. daily download record of 264K on Sept. 19, its 3rd straight day above 200K. ChatGPT didn’t cross 200K U.S. daily downloads until roughly a year after launch. Engagement is also running ahead: - Muse reached 448K U.S. DAUs by day 10 - ChatGPT took 49 days to reach roughly 450K U.S. DAUs - By day 12, Muse was at 642K U.S. mobile DAUs vs. 231K for ChatGPT at the same point - Even restricting Muse to U.S. iOS, DAUs were 359K Source: Sensor Tower

This CPU move is insane $AMD $META $INTC $ARM $RMBS Also the market seems to be appreciating $QCOM and $AIP as well today AIP finally hit my level I was watching this week after a few months QCOM I already own Nfa

I have a somewhat different view of where independent semiconductor research is heading. The problem institutional investors have isn't a shortage of research. It's the opposite. I've spoken with a number of hedge funds and institutional investors, and a recurring complaint is simply: too many reports. Their inboxes are overflowing with research they would like to read but realistically never will. AI is only going to increase that volume. So with SemiExponent, I'm building something deliberately different. Less publishing. More interaction. The institutional product is centered around direct access to semiconductor expertise: discussing technology, challenging assumptions, arguing through competing interpretations, and red-teaming an investment thesis when the underlying question is technical. In other words, not another research feed. A technical sparring partner. That model is intentionally high-touch, which also means SemiExponent will work with a relatively small number of institutional clients. I've been discussing the concept with several investment firms and am now beginning to launch it. If this sounds useful for your team, you can reach me through SemiExponent. Just leave a note with your email, I will get back to you.

i have been drawing the robotics stack as a set of layers but i might be wrong -> a useful robot is really 2 loops running at radically different speeds: 1. THE CONTROL LOOP (measured in milliseconds) sense the world -> decide -> command the joint -> apply force -> measure what happened -> correct so vision, encoders, force sensors, edge compute, motor control, and actuators all live inside this loop. If it's slow or unstable, the robot misses, slips, oscillates, collides, or falls 2. THE INDUSTRIAL LOOP (measured in weeks and months) design -> source -> assemble -> calibrate -> deploy -> fail -> diagnose -> repair -> redesign Manufacturing yield, end of line test, traceability, spare parts, technicians, and fleet data live inside this loop. If it's slow, the robot can look brilliant in a demo and still be a terrible business. Most robotics coverage focuses on the first loop because that is where intelligence is visible. My hunch is that the second loop will decide who scales The best robot will not only learn faster, it will be built, calibrated, fixed, and returned to work faster

did my homework on the robotics stack The visible robot is the last layer but there is a lot going on under the hood. The mistake is assuming value rises neatly from layer 1 to layer 6, as it moves toward whichever handoff is failing. Today that could be precision motion. At higher volume it may become calibration. After deployment it may become uptime and service. The robotics stack is a moving constraint Little breakdown below: 1. MATERIALS AND POWER Magnets, copper, bearings, batteries, lubricants, flex cables, and connectors set the physical boundary. 2. MOTION Motors, reducers, screws, brakes, and integrated actuators turn electricity into controlled force. 3. PERCEPTION AND CONTACT Cameras, lidar, encoders, force sensors, and tactile systems tell the robot what happened when it touched the world. 4. COMPUTE AND CONTROL Edge processors, motor drives, real-time networks, and policies close the loop fast enough to stay stable. 5. INDUSTRIALIZATION Assembly, calibration, burn-in, safety validation, traceability, and test turn components into repeatable machines. 6. DEPLOYMENT Workflow integration, uptime, teleoperation, repair, spares, and customer payback determine whether anyone orders the next fleet. full breakdown: