🤖 Can Codex Build a Human Pose Estimation App on reComputer R? From Natural Language Prompts to Edge AI in Action! Can Codex build and deploy a Human Pose Estimation app on the reComputer R? We put it to the test. With just a few prompts, Codex connected via SSH, built a YOLOv8 Pose Estimation WebUI, and deployed it directly to the device, powered by Hailo-8 acceleration. 🎯 Upload a video. Detect human poses. Visualize body keypoints and skeletons right in your browser. Less manual coding, faster prototyping, and Edge AI running where it matters. 🛠️ Check this project on reComputer AI Lab: #SeeedStudio# #reComputerR# #RaspberryPi# #reComputer# #VisionAI# #EdgeAI# #AIoT# #ComputerVision# #reComputerAILab# #AIDeployment# #PowerfulRaspberryPiAIBox# #TheAIHardwarePartner#

🚀 Deploy YOLO11 on RK3576 Module + Raspberry Pi CM4 IO Board!! We tested the #reComputer# #Rockchip# #RK3576# Module on a #RaspberryPi# #CM4# IO Board and successfully deployed #YOLO11# from #reComputerAILab# for real-time object detection! ✅ RK3576 NPU acceleration ✅ YOLO11 running locally on the edge ✅ USB camera support via CM4 IO Board ✅ Armbian + Docker deployment 🧩 Check reComputer RK3576 Module: 🛠️ Try reComputer AI Lab: 🛒 reComputer RK3576/RK3588 is in STOCK: #SeeedStudio# #reComputerRK# #Rockchip# #reComputer# #RK3576# #VisionAI# #EdgeAI# #AIoT# #ComputerVision# #reComputerAILab# #AIDeployment# #TheEasierPowerfulAIComputer# #TheAIHardwarePartner#

Run #LLMs# on @NVIDIARobotics #Jetson# without x86 ONNX export. 🚀 #TensorRT-Model-Connect# on reComputer Classic J5012 (#AGXOrin# 64GB, #JP7#.2). @huggingface checkpoint → trtmc build → .bundle on device trtmc run for text gen Tested: #Qwen3-4B# FP16 (~41GB container peak, 64GB recommended) No ONNX hop. Edge LLM/agent ready. Would you use on-device TRTMC for prototyping, or keep ONNX export for production? 👇 Full guide: Get your own one: #NVIDIA# #Jetson# #TensorRT# #TRTMC# #AGXOrin# #EdgeAI# #LLM# #PhysicalAI# #SeeedStudio#

Sealed. Fanless. Built for the real world. In #ports# and #logistics#, outdoor exposure and multi-camera setups are everyday challenges. The #reComputer# Rugged Series— powered by @NVIDIARobotics Jetson— handles them with #IP66# protection, lockable M12 connectors, and 4× PoE GbE ports. Bring #EdgeAI# where the work happens. Explore:

Seeed is headed to #IROS2026# in Pittsburgh. 🦾 📍 Booth #1107# · Sept 27 – Oct 1 As an #NVIDIA# #ElitePartner#, we're bringing: #reBot# Agentic demo— autonomous edge AI workflows #reSpeakerFlex# — on-device wake-word + beamforming #reComputerJ4012#/J5011/J6015 — #GMSL# vision & perception No slides. Just hardware that runs. See you there.🤝 #IROS2026# #PhysicalAI# #EdgeAI# #Jetson# #AIRobotics# #NVIDIA# #SeeedStudio# #TheAIHardwarePartner#

📡 A LoRaWAN gateway is more than a radio connected to the cloud. 🏭 See how WM1302, edge software, RS485, Ethernet and 4G work together inside the reComputer R1225. 💬 What matters most in your deployment: coverage, local processing or backhaul? 🔗 #SeeedStudio# #theAIHardwarePartner# #theIndieCommunicationSystem# #LoRa# #LoRaWAN# #LoRaWANGateway# #R1225# #WM1302# #SX1302# #IIOT# #BuildingManagementSystem# #SmartBuilding# #IoTGateway# #RaspberryPi#

For long-context LLM inference, should the KV cache be offloaded to disk or just recomputed on the GPU? There's no universal right answer, and this paper builds a system that decides quantitatively. Title: Building py-kvcache: A Performance Characterization of External KV Caching for vLLM with NVMe SSDs URL: 📝 Overview The paper proposes py-kvcache, an external KV cache system for vLLM. Using io_uring for async I/O, even a Python implementation pulls near-full SSD read bandwidth of 13.5GB/s. ❗ Problem it solves Existing external KV caches like LMCache have no criteria for when to actually use them — they load from disk unconditionally even when a short prefix or a fast GPU makes recomputation cheaper. ⚙️ Methodology It introduces "scheduler-aware preloading," which starts disk reads while a request is still waiting to be scheduled, plus a "break-even gate" that rejects a load whenever it wouldn't improve time-to-first-token. 📊 Results On LongBench multi-document QA it's 6.02-7.43x faster than GPU recomputation and 2.77-3.64x faster than LMCache. On multi-turn SCBench, native vLLM read 3.4TB from disk with completion times over 1200s, while py-kvcache kept disk reads to 85GB and completion time to 480s. 🖥️ Use cases On a high-end H100, requests often don't even clear the break-even point, so skipping external caching is fine — but on a lower-end RTX 4000 Ada, external caching clearly wins, giving concrete hardware-specific guidance. #LLMInference# #vLLM#

You do not pick a model and a GPU and call it done. You pick a file encoding, and a kernel path. The GPU follows those. Start with the loop. Text becomes tokens. Tokens move through a Transformer. Attention decides which earlier tokens matter. The runtime keeps a KV cache so the model does not recompute the whole conversation every time. Then it picks the next token and does it again. The model is not writing a whole answer in one shot. It is generating one token at a time. That loop has two phases, and they are not the same job. Prefill reads the prompt and builds the first KV cache. It is compute-heavy. That is the pause before the first word. Decode writes the answer one token at a time. It keeps rereading weights and the cache. It is bandwidth-heavy. So an RTX PRO 6000 with 1.8TB/s beats the DGX Spark with 273GB/s. That is the typing effect you feel. Long prompts punish prefill. Long answers punish decode. Long chats punish both, because the working memory grows. The inference engine is not the model. It is the traffic cop, the memory manager, the kernel dispatcher, the scheduler, the cache accountant, and the API surface. It loads the weights. It tokenizes the input. It runs the forward pass. It samples the next token. It keeps the KV cache. It streams the result. Serious engines also pick kernels. A kernel is not "the model." A kernel is a specific tensor program: shapes, layouts, datatypes, and what the silicon is actually allowed to multiply. Same math on paper. Different kernel. The file format matters. It decides what can load, what can quantize, and how fast it runs. Quantization is not one switch. Storing weights in 4-bit is not the same as doing 4-bit math. Weight quantization shrinks the model. The live context is a different thing. A Q4 sticker is not universal. The right format is the one your engine has optimized kernels for. Assuming every quantization label is portable is how people buy a 5090, download "NVFP4," and still miss out on performance. Here is the worked example. I put Qwen 3.8 27B on an RTX 5090. Two downloads. Same model. Same GPU. Both folders said NVFP4. One of them does 4-bit math for real. Weights in 4-bit. Activations in 4-bit. The matrix unit can multiply them as 4-bit times 4-bit. The other stores 4-bit weights. Then unpacks them in the kernel. Then does 16-bit math. Different kernel path. The 5090 did not choose that. The checkpoint did. Especially whether the activations are 4-bit too. If they aren't, there is no legal 4-bit times 4-bit multiply to run. The engine falls back. Quietly. The file still loads. That is the difference between an encoding you can load, an operation a backend implements, and arithmetic the hardware actually executes. They not interchangeable. This is also why prefill and decode do not get the same gift. Prefill has enough token rows to keep the matrix units busy. Native 4-bit math can matter there. Decode is still walking the weights and the cache, one token at a time. If the live state never went 4-bit, you do not get a 4-bit win on that part. The bottleneck moves. Do not benchmark "the model." Benchmark the stack you will actually run. Separate prefill from decode. One more thing people get wrong with the word Blackwell. It is a marketing name on four chips that cannot run each other's kernels. A data-center B200 is not a 5090. A 5090 is not a Spark. A Spark is not a Thor. Instruction support is necessary. It is not sufficient. A checkmark on the spec sheet is not the kernel that ran. In the inference engines article under my profile, I asked a question I still want on the wall: What quantization format has optimized kernels on my target engine? This Qwen run is that question in a box. Same GPU. Same NVFP4 label. Different kernels. The engine followed. The checkpoint decided which kernel it was allowed to launch. The file loaded but that is NOT the same as the kernel you wanted for the GPU you bought.

Most teams pull blockchain data from RPC providers, which can have bugs and bad configs. Amp, our blockchain data engine, builds verification into extraction. Amp recomputes the hash locally and verifies it against what's onchain. *Verification confirms the data matches the chain. Your analysts get plain SQL tables across multiple chains, in the BI tools they already use. Cloud, on prem, or hybrid.

New NanoGPT Speedrun WR at 76.3 (-2.9s) from @Lisennlp , with a radical boost to the final attention layer that drops step count by 7%, called lightweight Dynamically Composable MHA. A secondary local window (112 tokens) is recomputed on the same q/k. After softmax it mixes the per-head attn maps into one unified map, then applies the shared map to each head's values with a per-head scale. Every mix and scale operation is computed per-token from a MUDD MLP. All in a custom kernel. Takes inspiration from Dynamically Composable MHA