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Flexion Robotics, a startup founded by ex-Nvidia engineers, has a clever way of training robots to do useful work.

Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

Denormalization has been the standard approach to analytical data modeling for good reason. Moving joins, lookups, and business rules out of query time and into ingestion gives you the fastest possible reads for a known access pattern. For most of the past decade, it was often the practical default for latency-sensitive analytics. Earlier columnar engines and distributed query processors could execute joins, but many workloads paid for them through higher latency, higher compute cost, spill-to-disk, or distributed coordination overhead. That constraint has loosened. Modern columnar databases with advanced join algorithms have reduced the cost of runtime joins enough that normalization is now a genuinely viable option for many analytical workloads. Denormalization still delivers faster reads, but normalization can bring operational benefits: simpler pipelines, flexible schemas, and cleaner governance. Engineers can now make the decision based on their actual workload characteristics, rather than being forced into one approach by engine limitations. This guide is a decision framework for making that choice in ClickHouse. It starts with why denormalization became the default, explains what has changed in join performance, then compares the tradeoffs on both sides so you can decide where to denormalize, where to join, and where to use ClickHouse primitives that bridge the gap. For a broader evaluation framework covering latency, concurrency, ingest throughput, SQL flexibility, and cost across real-time OLAP options, see our guide to choosing a database for real-time analytics in 2026 . For a deeper comparison of how ClickHouse executes star schema joins against Druid, Pinot, and cloud DWHs, see our star schema and fast joins guide . TL;DR Denormalization and normalization are both valid modeling strategies. The right choice depends on your workload. Denormalization's tradeoffs are primarily operational : pipeline complexity, write-path overhead, data freshness lag, back

GitHub热门项目 | 15MB, lightweight, cross-platform database client. Supports MySQL, PostgreSQL, SQLite, Redis, MongoDB, DuckDB, ClickHouse, SQL Server and more.15MB，轻量级跨平台数据库客户端、数据库管理工具。支持 MySQL、PostgreSQL、SQLite、Redis、MongoDB、DuckDB、ClickHouse、SQL Server 等。 | Stars: 7,530 | 1,100 stars this week | 语言: Rust

We're upgrading Crawlberg to a new version: Crawlberg v1.0.0. It builds on the previous kreuzcrawl. It declares the public API frozen under the new project name. All technical features below shipped in v0.3.0 (2026-06-23); v1.0.0 is a stability declaration and rename, not a new feature release. The four production-facing changes most likely to require operational action: Package and env var rename - every artifact identifier has changed; see the migration table. SSRF defense is now on by default - internal crawl targets (localhost, RFC 1918, cloud metadata) will fail without CRAWLBERG_ALLOW_PRIVATE_NETWORK=1 . CrawlError::WafBlocked is now a struct variant - exhaustive match arms will not compile until updated. max_retries semantics changed - off-by-one fixed; max_retries=3 now produces exactly 3 retries. Precompiled binaries cover Linux (x86_64/aarch64), macOS (ARM64 and x86_64), and Windows x64. Homebrew bottles and Docker images on GHCR are also available. What Is Crawlberg? Crawlberg is a web crawling engine written primarily in Rust that exposes a single consistent API across 14 language runtimes. It handles HTTP transport, JavaScript rendering, robots.txt compliance, per-domain rate limiting, SSRF safety, and structured extraction. Extension points ( Frontier , RateLimiter , CrawlStore , EventEmitter , ContentFilter , WafClassifier , ProxyProvider ) are injectable traits; wire in your own frontier, storage backend, or proxy pool without forking the engine. A single scrape() call returns text, metadata, links, images, assets, JSON-LD, Open Graph tags, hreflang, favicons, headings, response headers, and clean HTML→Markdown. When a site requires JavaScript, the optional headless browser tier handles it transparently. v1.0.0 promotes v1.0.0-rc.2 and freezes the public API under the new project name. The features described in the sections below represent the platform that 1.0.0 declares stable; they shipped in v0.3.0. What v1.0.0 Declares Stable These capabilities

When the enemy is too strong to attack directly, attack what they hold dear. They will come to you...

In the world of wearable health technology, the holy grail has always been moving intelligence from the cloud to the edge. Waiting for a cloud server to analyze your heart rhythm is not just a latency issue—it's a privacy and battery life concern. Today, we are diving deep into TinyML , Edge AI , and ECG signal processing to build a real-time abnormality detector. By leveraging TensorFlow Lite for Microcontrollers and the versatile ESP32 , we can process raw electrocardiogram (ECG) data locally. This approach ensures low-latency detection of arrhythmias while keeping sensitive medical data on-device. If you've been looking to bridge the gap between high-level deep learning and low-level embedded systems, you're in the right place! The Architecture: From Raw Signal to Insight 🏗️ The pipeline involves capturing a high-frequency analog signal, cleaning it, and feeding it into a quantized Convolutional Neural Network (CNN). Here is how the data flows through our ESP32: graph TD A[Raw ECG Signal/Sensor] -->|ADC Sampling| B(Preprocessing: Bandpass Filter) B --> C{Buffer Management} C -->|Windowed Segment| D[TFLite Micro Inference Engine] D --> E{CNN Model Classification} E -->|Normal| F[Log: Sinus Rhythm] E -->|Abnormal| G[Trigger Alert: Arrhythmia] G -->|Bluetooth/Wi-Fi| H[Mobile Dashboard] Prerequisites 🛠️ To follow this advanced guide, you'll need: Hardware : ESP32 (DevKit V1 or similar). Sensor : AD8232 ECG Module (or simulated ECG data). Software : Arduino IDE or PlatformIO. Frameworks : TensorFlow Lite for Microcontrollers (TFLM), EloquentTinyML (optional wrapper), or the standard C++ TFLM library. Step 1: Model Training & Quantization 🧠 Before we touch the C++ code, we need a model. Typically, we use the MIT-BIH Arrhythmia Database to train a 1D-CNN. The crucial step is Post-Training Quantization . Since the ESP32 doesn't have a dedicated NPU, we convert our 32-bit float model into an 8-bit integer (INT8) model. This reduces the size by 4x and speeds up inference s

you've felt it. you type a prompt, hit send, and the response starts streaming in under a second....

Suno has ambitions to be more than just a toy to churn out AI slop, it also wants to be a streaming destination and to break new artists. Spark is their new incubator program for independent artists that provides grants, mentorship, and marketing support. To apply, artists need to be an unsigned singer, songwriter, or […]

This article covers debugging and deploying a Rust backed WASM module with a Firebase hosted web app...

GitHub热门项目 | A simple, fast and user-friendly alternative to 'find' | Stars: 43,499 | 16 stars today | 语言: Rust

GitHub热门项目 | BitFun is a desktop-grade Agent runtimeand a ready-to-use suite of desktop Agent applications.with built-in Code Agent 、 Cowork Agent、Computer Use. It has memory, personality, and the ability to evolve over time | Stars: 1,243 | 23 stars today | 语言: Rust

GitHub热门项目 | A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ... | Stars: 32,389 | 7 stars today | 语言: Rust

GitHub热门项目 | Fullstack app framework for web, desktop, and mobile. | Stars: 36,549 | 16 stars today | 语言: Rust

GitHub热门项目 | The Mullvad VPN client app for desktop and mobile | Stars: 7,292 | 59 stars today | 语言: Rust

GitHub热门项目 | Ultra-fast, simple and powerful cross-platform IPTV app | Stars: 3,668 | 28 stars today | 语言: Rust

GitHub热门项目 | A scalable, distributed, collaborative, document-graph database, for the realtime web | Stars: 32,510 | 30 stars today | 语言: Rust

Where to eat, stay, work, and eat some more while visiting Space City on business.

The Chinese supercomputer LineShine was ranked as the fastest in the world, despite not using any GPUs.