与「controller」相关的搜索结果

以防有人不知道，美国几乎每个州都有 #无人认领财产# 截至目前，纽约州已累计归还超过 190 亿美元的无人认领财产 还有约 200 亿美元等待认领 平均每天返还超过 200 万美元 💰 有空可以搜一下自己的名字，说不定还能找回一笔钱呢～ 比如政府退款、退税、工资支票、银行账户余额、保险理赔、押金等等，都有可能 加州也很多，是 California State Controller’s Office 管理的官方网站，已经帮民众领回超过 83.8 亿美元 查询完全免费‼️ 没有申请期限 只要输入姓名就能查看 记得一定要使用官方网站，不要找第三方代办，更不要支付任何查询费用 如果曾经在美国生活、工作或留学过，不妨花一分钟查一下，说不定会有意外惊喜 其他州也都有类似的官方查询网站，可以一起搜搜看

The Backbone Pro is a premium controller that offers a slick, comfortable and clever experience for mobile gaming on iPhone or Android. Check it out now #gaming# #backbonepro# #controller# #mobilegaming# #gamingaccessories#

What if building a robot was as simple as describing it? I typed a single sentence: "Build a quadruped robot that walks a figure 8." Everything else was engineered automatically. @pyroscli doesn't just generate robot code. It executes the entire robotics engineering pipeline. It designs the robot's morphology / generates the geometry and inertias / solves and validates the kinematics / sizes the actuators / plans a dynamically feasible gait / tunes the controller and runs the simulation. Every dimension / torque / mass and trajectory comes from deterministic engineering tools with traceable sources not language-model guesses. In this demo, you'll watch the robot assemble in real time as the engineering pipeline executes, then immediately begin walking a smooth figure-8 trajectory. The goal isn't to produce another URDF that looks correct. The goal is to produce a robot that is mechanically consistent, mathematically verified, and ready to build. Natural language in. Verified robot out.

助力国产百元手柄平替Codex Micro，开源Agent controller。只要人民币，不要230美元。甚至无需运费 大致思路是，将手柄操作转成Codex操作。目前仅支持Windows 64位，由于是一天内（让Codex）赶工出来的，会有不少问题，开软件，重启Codex(ChatGPT)会好很多 开源地址：

remember the OG XBOX 360 i bought at a garage sale for $40??? i finally booted it up and IT HAS THE OG DASHBOARD somehow hasnt been updated?!?! also brought out my 2012 XBOX 360 SCUF controller, good times ~

Memory cost and capacity are significant issues for AI accelerators. Unlike game rendering, model inference can have a deterministic memory access pattern. You don’t need “random access memory” at all for model weights, and you could tolerate cold-start latencies in the multiple milliseconds, as long as continuous reads were delivered at the necessary bandwidth. NAND flash is over 100 times cheaper per GB than HBM, so there should be opportunity there, even after giving a flash controller a 1024 bit interface with HBM bandwidth. You could make a specialized pin protocol that just supported pipelined transfer of full 16KB+ pages from the flash to program-managed accelerator scratchpad memory and improve per-pin performance over HBM, but it might be more convenient to make it still look like a true random access memory with very fragile performance characteristics, where anything but sequential reads falls off a 1000x+ performance cliff. That has the advantage of automatically using existing cache hierarchies, and providing a natural path to update the flash memory with new model weights. With the stream-to-scratch interface, code has to be completely rewritten before it works at all, while the ram-emulation interface will start off just extremely slow, and you can incrementally sort out the changes for full performance. There may be cases where there isn’t enough scratchpad SRAM to hold the weights for a layer, which might force you to deploy the old optical drive optimization technique of duplicating data in multiple places on a sequential read to avoid seeking, but there would be capacity to burn. It might be possible to do something like cuda graph capture to record a memory access trace and have everything magically remapped to a linear sequence, but deploying programmer / agent elbow grease to manage transfers and access in a scratch ram ring buffer would be lower risk. A split memory system consisting of some channels of flash and some channels of HBM will probably be suboptimal compared to a uniform memory, but it could be much cheaper, and allow much larger models to be run. I think th case is strong for inference, but you have to stretch more for training. You can still linearize all the weight memory accesses, both reads and writes, but flash memory would quickly wear out from the writes, even if they were all perfectly page aligned. Replacing low-latency HBM with massively parallel cheap(er) DRAM at high latency might still be a worthwhile cost savings.

A 19-year-old turned his phone into the controller for a real drone in 4 hours with Claude The drone was in his hand. The simulator was on his MacBook. The phone bridged them His name is Kai. The drone is a $99 DJI Tello sitting on his desk. The MacBook is running a custom 3D drone simulator with a green wireframe grid. The phone is showing a virtual joystick interface he built that morning The simulator on screen shows the drone hovering above the grid. Kai's thumb is on his phone. The drone on the MacBook tilts forward. The physical drone in his other hand spins its props at the same angle One input. Two drones move. Sim and real, synced. He built all three pieces with Claude Opus 4.8 in one session. He typed three prompts. Total "Build me a 3D drone simulator in the browser. Use Three.js. Quadcopter physics, gravity, drag, motor RPM per axis. Green wireframe environment." "Now add a websocket server so a phone can control the drone in this sim. Phone shows a joystick UI. The drone in the browser reacts in real time." "Now make the same websocket also send the same control values to a real drone over the Tello SDK. Make the sim and the real drone fly in parallel from the same input." Claude wrote the Three.js sim. The websocket server. The mobile joystick UI. The UDP bridge to the physical drone. The drift correction loop. All of it Kai did not write a line of code. Claude told him which files to paste into 4 hours from "I wonder if I could" to a phone flying a real drone through a wireframe world The simulator alone would have been a $4,000 freelance project last year. The control bridge would have been another $6,000. Most senior devs would have quoted 3 weeks Kai shipped it the same Saturday He posted a 28-second clip to TikTok that night 1.9M views in 36 hours 4 drone hardware startups in his DMs by morning. Two offered to fly him out. One offered him equity He is 19. He is still in college. He took the meeting from his dorm room with the same drone hovering behind him His CS professor had told the class that semester that AI would "augment programmers, not replace the discipline." Kai built more shippable software in one Saturday than he had built in 3 semesters of coursework The internet said building real-world hardware control needed embedded engineers and months of firmware work He did it on a school laptop with a model that wrote every line