Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

A small, inexpensive but high quality AI Pendant which stores and analyzes sound after the user presses a button. Operates via tether to phone.

This project is a Wearable AI Camera designed to integrate multiple components such as a Murata Bluetooth module, Crypto controller, and various sensors like the STMicroelectronics Time-of-Flight sensor and microphone. It's powered by a diverse set of power nets and connects through different communication protocols. #wearableDevices

This project is a DC-DC Buck converter based on the LM2596 IC. It is designed to step down the input voltage from 12V to a regulated output of 5V #Buck #LM2596 #project

ESP32-C3 Espresso Smart Scale --------------------------------------------- Powered by TinyML so you never pull a sour shot again.

Fully-Integrated Bi-directional PD3.0 and Fast Charge Power Bank SOC with Multiple Input and Output Ports based on IP5328P

MCU Footprint Update – Verified with No New DRC Violations

Cost-Effective USB-C 2S Li-Ion Charger with Integrated 1W LED Flashlight - Fully Discrete, Generic Components, Minimalist Design

Welcome to your new project. Imagine what you can build here.

1. Empieza con el objetivo Ejemplo: “Estoy creando un módulo de control para una bomba de aire de 24 V en una máquina CNC láser. El circuito debe encender y apagar la bomba según la señal FAN que viene de la tarjeta de control (3.3 V o 5 V).” 2. Explica los requerimientos La bomba trabaja a 24 V y hasta 2 A. El control debe ser con un MOSFET N–channel en conmutación. Debe incluir protección contra picos y ruidos eléctricos. Se deben mostrar indicadores LED (encendido, funcionamiento, error). 3. Lista de funciones que quieres en el diseño Protección: fusible, diodo flyback, TVS, snubber RC. Control: MOSFET con resistencia de gate y pull-down. Filtrado: capacitores cerca de la bomba. Indicadores LED: Azul: energía 24 V presente. Verde: bomba activa. Rojo: error o apagado. 4. Explica la lógica de funcionamiento (qué debe pasar) Cuando la fuente 24 V se conecta → LED azul enciende. Cuando la señal FAN activa el MOSFET → bomba enciende + LED verde enciende. Cuando la bomba está apagada → LED rojo puede encender (opcional). Si ocurre sobrecorriente → el fusible abre el circuito. 5. Diagrama de bloques sencillo (texto) [FUENTE 24V] -- [FUSIBLE] --+--> [BOMBA] --> [MOSFET] --> GND | +--> [LED Azul] --> GND [SALIDA FAN] --> [Res 100Ω] --> [Gate MOSFET] [Gate MOSFET] --> [Pull-down 100kΩ a GND] [Protecciones: Diodo, TVS, RC, Capacitores en paralelo con la bomba]

make this for me now # Device Summary & Specification Sheet ## 1. Overview A rugged, Arduino-Uno-and-Raspberry-Pi-style single-board micro-PC featuring: - Smartphone-class CPU (Snapdragon 990) - USB-C Power Delivery + 4×AA alkaline backup + ambient-light harvester - On-board Arduino-Uno-compatible ATmega328P - External NVMe SSD via USB3 bridge & optional Thunderbolt 3 eGPU support - 5× USB 3.0 ports, HDMI in/out, Gigabit Ethernet & SFP fiber, Wi-Fi, Bluetooth, LoRa - 0.96″ OLED status display, 3.5 mm audio jack with codec --- ## 2. Key Specifications | Category | Specification | |--------------------|-------------------------------------------------------------------------------| | CPU | Snapdragon 990, octa-core up to 2.84 GHz | | Memory | 6 GB LPDDR4x DRAM | | Storage Interface | PCIe Gen3 ×4 → M.2 NVMe + USB 3.1 Gen1 bridge | | MCU | ATmega328P (Arduino-Uno-compatible) | | Power Input | USB-C PD up to 20 V/5 A; 4×AA alkaline backup; ambient-light photodiode boost | | Power Rails | 12 V, 5 V, 3.3 V, 1.8 V, 1.2 V via buck/buck-boost regulators | | USB Hub | 5× USB 3.0 downstream ports | | Display | 0.96″ 128×64 OLED via I²C/SPI | | Networking | 1 × Gigabit RJ45; 1 × SFP fiber; Wi-Fi 802.11ac + Bluetooth; LoRa SX1276 | | Video I/O | HDMI 2.0 input (RX) & output (TX) | | Audio | 3.5 mm jack + TLV320AIC3101 codec; Bluetooth audio | | Form Factor | Raspberry Pi–style header + Arduino-Uno shield headers; 4× standoff mounts | --- ## 3. Complete Parts List | Part | Function | Qty | |------------------------------------------------------------------------------------------------|-----------------------------------------------|-----| | [Snapdragon 990](https://www.flux.ai/search?type=components&q=Snapdragon%20990) | Main application CPU | 1 | | [LPDDR4x DRAM](https://www.flux.ai/search?type=components&q=LPDDR4x%20DRAM) | System memory | 1 | | [eMMC 64GB](https://www.flux.ai/search?type=components&q=eMMC%2064GB) | On-board storage | 1 | | [M.2 NVMe Connector](https://www.flux.ai/search?type=components&q=M.2%20NVMe%20Connector) | External SSD interface | 1 | | [JMS583](https://www.flux.ai/search?type=components&q=JMS583) | PCIe→USB 3.1 bridge for NVMe | 1 | | [Titan Ridge](https://www.flux.ai/search?type=components&q=Titan%20Ridge) | Thunderbolt 3/eGPU controller | 1 | | [STUSB4500](https://www.flux.ai/search?type=components&q=STUSB4500) | USB-C Power-Delivery controller | 1 | | [LTC4412](https://www.flux.ai/search?type=components&q=LTC4412) | Ideal-diode OR-ing | 1 | | [LTC3108](https://www.flux.ai/search?type=components&q=LTC3108) | Ambient-light (solar) energy harvester | 1 | | [Battery Holder 4×AA](https://www.flux.ai/search?type=components&q=Battery%20Holder%204xAA) | Alkaline backup power | 1 | | [TPS53318](https://www.flux.ai/search?type=components&q=TPS53318) | 6 V→5 V synchronous buck regulator | 1 | | [MCP1700-3302E/TO](https://www.flux.ai/search?type=components&q=MCP1700-3302E/TO) | 6 V→3.3 V LDO | 1 | | [TPS63060](https://www.flux.ai/search?type=components&q=TPS63060) | Buck-boost for 12 V rail (eGPU power) | 1 | | [ATmega328P](https://www.flux.ai/search?type=components&q=ATmega328P) | Arduino-Uno microcontroller | 1 | | [ESP32-WROOM-32](https://www.flux.ai/search?type=components&q=ESP32-WROOM-32) | Wi-Fi + Bluetooth co-processor | 1 | | [SX1276](https://www.flux.ai/search?type=components&q=SX1276) | LoRa transceiver | 1 | | [TUSB8041](https://www.flux.ai/search?type=components&q=TUSB8041) | 5-port USB 3.0 hub IC | 1 | | [Ethernet PHY](https://www.flux.ai/search?type=components&q=Ethernet%20PHY) | Gigabit Ethernet physical transceiver | 1 | | [SFP Cage](https://www.flux.ai/search?type=components&q=SFP%20Cage) | Fiber-optic SFP module connector | 1 | | [TDA19978](https://www.flux.ai/search?type=components&q=TDA19978) | HDMI 2.0 receiver (input) | 1 | | [TFP410](https://www.flux.ai/search?type=components&q=TFP410) | HDMI 2.0 transmitter (output) | 1 | | [TLV320AIC3101](https://www.flux.ai/search?type=components&q=TLV320AIC3101) | Audio codec for 3.5 mm jack & Bluetooth audio | 1 | | [Audio Jack 3.5mm](https://www.flux.ai/search?type=components&q=Audio%20Jack%203.5mm) | Headphone/mic port | 1 | | [SSD1306 OLED](https://www.flux.ai/search?type=components&q=SSD1306%20OLED) | Status display | 1 | --- ## 4. High-Level Design & Implementation Steps 1. **Schematic Creation** - Section off blocks: CPU + memory, power-supply, NVMe/USB bridge, MCU, wireless, USB hub, video, audio, I/O headers. - Place all parts from the list and wire per respective datasheet reference circuits. 2. **Power System** - Implement USB-C PD with STUSB4500 → buck/buck-boost & LDO rails. - Add LTC4412 to OR-ing battery pack into 6 V bus. - Include LTC3108 harvest input to 6 V bus (no recharge to AA). - Add decoupling & bulk caps per regulator datasheets; PTC fuses & ESD diodes on inputs. 3. **SoC & High-Speed Interfaces** - Break out Snapdragon 990 BGA with DDR4 routing (point-to-point). - Route PCIe Gen3 lanes to M.2 slot via JMS583 and to Titan Ridge for TB/eGPU. - Follow high-speed layout guidelines: impedance, length matching, EMI filters. 4. **MCU & Expansion Headers** - Place ATmega328P with UNO-style 2.54 mm headers; add ISP 6-pin, I²C pull-ups, reset circuit. 5. **Connectivity Blocks** - USB hub: TUSB8041 + ESD + chokes → five USB-A 3.0 ports. - Ethernet: RJ45 + magnetics + PHY; SFP cage + PHY/SFP interface. - Wireless: ESP32 module pad + SX1276 + antenna matching; program pads. 6. **Video & Audio** - HDMI RX (TDA19978) & TX (TFP410) blocks with TMDS ESD & termination. - Audio codec + 3.5 mm jack; route differential audio lines; add ESD. 7. **Display & Indicators** - SSD1306 OLED on I²C; add pull-ups & backlight power if needed. - LEDs for power, charge, status. 8. **PCB Layout & Mechanical** - Follow Raspberry Pi 2×20 header footprint & UNO shield hole pattern. - Add reinforcement for rugged use; standoffs at corners. - Define 4-layer stackup: • Top: signal • L2: GND • L3: PWR • Bottom: signal 9. **DRC/ERC Reviews** - Run ERC for decoupling, pull-ups, power nets. - Run DRC for high-speed clearance, differential pairs, via aspect ratios. 10. **Fabrication & Testing** - Export Gerbers, IPC-2581, BOM. - Prototype assembly; verify power sequencing, rails, interfaces. - Functional tests: USB-C PD negotiation, NVMe throughput, eGPU over TB3, wireless comm, audio, OLED. --- This spec sheet and part list should guide your Flux schematic and PCB layout workflow through to fabrication and validation. Let me know if you’d like to deep-dive into any specific block or review datasheet details next!

Welcome to your new project. Imagine what you can build here.

Learn how to optimize your project for cost with this Vehicle Display System project that was open sourced from the Tesla Roadster. Optimizing your BOM for cost can take forever to research component alternatives and understand the supply chain. Learn how to optimize for cost in seconds with Flux Copilot.

Welcome to your new project. Imagine what you can build here.

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

1. Overview: The Pundit pendant is a wearable AI transcription assistant. An innovative device designed to seamlessly integrate into daily activities, providing real-time transcription and note-taking capabilities. Combining advanced AI algorithms with state-of-the-art hardware components, the device offers crystal clear audio recording, durable construction, and convenient features such as cloud synchronization, weatherproofing, and a vibrant display for animations and expressions. 2. Hardware Specifications: * Rechargeable Battery: Lithium-ion battery providing up to 150 hours of continuous operation. * Construction: Durable aluminum body ensuring longevity and protection against wear and tear. * Audio Quality: High-fidelity microphone array for clear and accurate transcription, with noise cancellation technology. * Weatherproofing: Sealed construction to withstand various weather conditions, making it suitable for outdoor use. * Versatile Mounting: Equipped with a magnetic clasp for easy attachment to clothing or accessories. * Connectivity: Wi-Fi and Bluetooth connectivity for seamless data transfer and integration with other devices. * Charging: USB-C port for fast and convenient charging, with support for various power sources. * Input Microphone Array: Multiple microphones strategically placed for optimal audio capture and transcription accuracy. * Display: Colorful screen for displaying animations, expressions, and status indicators, enhancing user interaction and personalization. 3. Software Features: * Real-time Transcription: Utilizes AI algorithms for instant transcription of spoken words into text, with high accuracy. * Note-taking: Automatically creates and organizes notes based on conversations, timestamps, and contextual cues. * Audio Recording: One-touch button for initiating audio recording, with options for manual or automatic saving. * Cloud Synchronization: Syncs transcription data to the cloud for easy access and retrieval from any device. * Speech Recognition: Advanced speech recognition technology for identifying speakers and distinguishing between multiple voices. * Language Support: Multilingual support for transcription and note-taking in various languages. * Customization: User-configurable settings for adjusting transcription preferences, language models, and display animations. * Security: Encryption and authentication protocols to ensure the privacy and security of transcription data. 4. Dimensions and Weight: * Dimensions: Compact and lightweight design for comfortable wearability. * Weight: Minimal weight to prevent discomfort during prolonged use. 5. Compatibility: * Operating Systems: Compatible with iOS, Android, and other major operating systems. * Applications: Integration with popular productivity and communication apps for seamless workflow management. 6. Warranty and Support: * Warranty: Manufacturer's warranty covering defects in materials and workmanship. * Support: Dedicated customer support for technical assistance, troubleshooting, and software updates. 7. Target Market: * Professionals: Ideal for professionals in various industries, including journalists, researchers, students, and business professionals. * Outdoor Enthusiasts: Suitable for outdoor activities such as hiking, camping, and fieldwork where reliable transcription and note-taking are essential. * Everyday Users: Provides convenience and efficiency for everyday tasks, such as meetings, lectures, and personal reminders. 8. Conclusion: The Wearable AI Transcription Assistant sets a new standard for wearable technology, offering unmatched transcription and note-taking capabilities in a compact and durable package. With its advanced features, seamless connectivity, vibrant display, and user-friendly design, it is poised to revolutionize how we capture and manage information in our daily lives while adding a touch of personality and fun with customizable animations and expressions.

The Ariel AI Chip, a state-of-the-art integrated circuit designed for high-performance computing applications, incorporates an innovative architecture that leverages radical transistor technology to optimize AI and machine learning tasks. At the heart of this chip lies a quad-core CPU operating at a clock speed of 2GHz, distinguished by its part number CPU-RT-4C-2G. The chip's power management is efficiently handled by a DC power supply, specified as DCPS-5V, ensuring a stable 5V input. Key to its operation are two NPN transistors, identified by part numbers NPN-TRANS-001 and NPN-TRANS-002, which, along with a pair of 1kΩ resistors (RES-1K and RES-1K-002) and a 10µF capacitor (CAP-10UF), form the critical signal processing and conditioning circuitry. This assembly is designed for seamless integration into advanced computing systems, particularly those focused on Flux AI environments, where its performance and efficiency can be fully leveraged. The Ariel AI Chip sets a new benchmark in AI computing, offering unparalleled processing power and efficiency for cutting-edge applications.

The semgdaq board is a wearable 6 channel data acquisition unit for capturing surface electromyographic (sEMG) signals from human arm muscles using SJ2-3593D jack connectors while conditioning, digitizing, processing and feature extracting them then transmitting the feature data as vectors to an external AI accelerated board through an SM12B-SRSS IDC connector using 12C and UART communication protocals where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The feature vectors are comprised of onset detection, slope sign changes, autoregression coefficients and Short Time Fourier Transform magnitude spectrum data for each segment or window of the signals in real time. This vectors can be used as the basis for further feature extraction on more computationally resourceful hardware where machine learning algorthms can be employed for descision making in the applications mentioned earlier. The board leverages INA125P instrumentation amplifiers together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing, feature extraction and data transmission. Since AI models can only be as good as the data, the design of such a DAQ is necessary to ensure clean, reliable and real-time data for AI applications requiring sEMG feature data. The board also has USB-FS and JTAG to cater for debugging and external flash memory to extend its data storage and processing capability. The power (5V) is fed through a screw terminal and is regulated by two LDK320AM LDO regulators to offer 5V, 3.3V and 1.8V to meet the requirements of various components on the board.

The Ariel AI Chip, a pioneering component in the realm of artificial intelligence hardware, integrates a suite of electronic elements tailored for high-performance computing applications. At the heart of this assembly lies a CPU with a Radical Transistor architecture, featuring a quad-core setup clocked at 2GHz, identified by the part number CPU-RT-4C-2G. Power management is facilitated through a DC Power Supply, marked DCPS-5V, ensuring a stable 5V supply to the intricate circuitry. The chip's switching capabilities are bolstered by two NPN transistors, NPN-TRANS-001 and NPN-TRANS-002, which play a crucial role in signal modulation. Essential to the chip's operation are the passive components: two 1kΩ resistors (RES-1K and RES-1K-002) and a 10µF capacitor (CAP-10UF), which together with the transistors, form a robust network ensuring reliable performance under varying load conditions. Designed for integration into advanced AI systems, this chip stands out for its innovative use of standard components in a configuration that emphasizes efficiency, reliability, and high-speed data processing capabilities.

The Ariel AI chip prototype, designed for integration with Flux AI for advanced simulation and testing, incorporates a suite of electronic components optimized for high-performance computing applications. At the heart of this system lies a CPU with a radical transistor architecture, featuring a 4-core configuration and a clock speed of 2GHz, identified by part number CPU-RT-4C-2G. Power management is facilitated through a DC Power Supply, specified as DCPS-5V, ensuring a stable 5V supply to the system. The circuit's dynamic performance is modulated by two NPN transistors, NPN-TRANS-001 and NPN-TRANS-002, which, along with precision resistors RES-1K and RES-1K-002 (both 1kΩ), and a 10μF capacitor (CAP-10UF), form the critical signal processing path leading to the CPU. This configuration is designed to provide an efficient, reliable processing environment for AI computations, with an emphasis on minimizing latency and maximizing throughput. The Ariel AI chip's architecture, combining traditional components with an innovative CPU design, offers a versatile platform for developing advanced AI applications, reflecting a significant step forward in computational technology.

The Ariel AI Chip, a pioneering component in the field of artificial intelligence hardware, integrates advanced features designed to enhance computational efficiency and AI processing capabilities. This chip is distinguished by its utilization of a quad-core CPU with a clock speed of 2GHz, operating on a radical transistor architecture that promises significant improvements in speed and power efficiency. Key components that constitute the Ariel AI Chip include a DC power supply with a 5V output (DCPS-5V), NPN transistors (NPN-TRANS-001 and NPN-TRANS-002) that serve as the fundamental switching elements, precision resistors (RES-1K and RES-1K-002) each with a resistance of 1kΩ, and a capacitor (CAP-10UF) rated at 10μF to stabilize voltage and filter noise. This chip is designed for integration into systems requiring advanced AI capabilities, offering a comprehensive solution for developers looking to leverage machine learning and artificial intelligence in their applications. With its innovative architecture and component selection, the Ariel AI Chip stands out as a versatile and powerful tool for a wide range of AI applications, from embedded systems to more complex computational platforms.

The Ariel AI Chip, an innovative component designed for high-performance computing applications, integrates a sophisticated array of electronic parts to deliver unparalleled processing capabilities. At the heart of this system is a CPU with a radical transistor architecture, featuring a core count of 4 and a clock speed of 2GHz, identified by its part number CPU-RT-4C-2G. Power management within the chip is efficiently handled by a DC Power Supply, rated at 5V, with the part number DCPS-5V, ensuring stable and reliable operation. The chip's signal processing and amplification needs are addressed through the inclusion of two NPN transistors, with part numbers NPN-TRANS-001 and a similar variant, providing the necessary gain and switching capabilities for complex computational tasks. Signal conditioning is further enhanced by a pair of 1kΩ resistors, RES-1K and RES-1K-002, and a 10µF capacitor, CAP-10UF, which work together to filter and stabilize the power supply and signal pathways, ensuring clean and noise-free operation. This integration of components within the Ariel AI Chip offers electrical engineers a robust platform for developing advanced AI systems, combining high processing power with efficient power management and signal integrity, suitable for a wide range of applications in the field of artificial intelligence.

Seeed Studio XIAO ESP32S3 leverages dual-core ESP32S3 chip, supporting both Wi-Fi and BLE wireless connectivities, which allows battery charge. It integrates built-in camera sensor, digital microphone. It offers 8MB PSRAM, 8MB FLASH, and external SD card slot. All of these make it suitable for embedded ML, like intelligent voice and vision AI. #SeeedStudio #xiao

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

The Ariel AI chip prototype is an advanced electronic component designed for integration into the Flux AI environment, facilitating simulation and testing of AI applications. This component features a collection of carefully selected parts including a DC power supply (DCPS-5V), NPN transistors (NPN-TRANS-001 and NPN-TRANS-002), resistors (RES-1K and RES-1K-002), a capacitor (CAP-10UF), and a cutting-edge CPU (CPU-RT-4C-2G) with a 4-core architecture, operating at a clock speed of 2GHz. The CPU's innovative radical transistor architecture is specifically tailored for high-performance computing tasks associated with AI and machine learning applications. This configuration ensures efficient power management, signal processing, and data flow within the chip, making it an ideal choice for developers and engineers looking to push the boundaries of AI technology. The inclusion of standard components like NPN transistors, resistors, and capacitors, alongside the specialized CPU, allows for a versatile and robust design, suitable for a wide range of AI applications.

The Ariel AI chip prototype is an advanced electronic component designed to enhance the capabilities of Flux AI systems through a sophisticated arrangement of transistors, resistors, capacitors, and a cutting-edge CPU. Key components include two NPN transistors (part numbers NPN-TRANS-001 and NPN-TRANS-002), which are essential for signal amplification, alongside precision resistors (RES-1K and RES-1K-002) each with a resistance of 1kΩ, and a capacitor (CAP-10UF) with a capacitance of 10μF, crucial for filtering and stabilizing the voltage supply. At the heart of the design is a revolutionary CPU (part number CPU-RT-4C-2G) featuring a quad-core setup with a clock speed of 2GHz, based on a radical transistor architecture, designed to deliver unparalleled computational performance for AI tasks. This component set is powered by a 5V DC power supply (DCPS-5V), ensuring a stable and efficient operation. The Ariel AI chip is engineered for high-speed, reliable performance in demanding AI applications, representing a significant advancement in electronic component design for artificial intelligence systems.

I designed a Raspberry Pi pico-like board using only AI

Daddy's second circuit board. A sign to let my wife know when I'm on a call. Activates with a slide switch and is powered by USB-C.

Welcome to your new project. Imagine what you can build here.

R2 w Thread changes: -Moving to Letter Modules for ease of design -Adding MGM210L for Matter on Thread On/Off and intensity control -Shifted A and R letters closer to fix Kerning -Optional: Add unpopulated AA Battery Holder for battery option R1 changes: -Changed LED part to Red LEDs -adjusted resistor value of buck converter -Changed source for USB-C Connector -Removed exposed soldermask on buck converter with negative soldermask expansion -Order with black soldermask Modified by markwu2001: - Adjustable Brightness, - 85-90% Drive Efficiency - <5W Operation (Can use 5V 1A Plug) This project can be purchased from LCSC Original Description: Daddy's second circuit board. A sign to let my wife know when I'm on a call. Activates with a slide switch and is powered by USB-C. #template #arduino-matter

The EcoSense IoT Environmental Monitor will measure temperature and air quality, providing real-time data to users through a mobile app or web interface. The device will be compact, easy to install, and user-friendly, offering insights into the indoor environmental conditions to promote health and well-being. When answering any questions, make sure you speak in highly technical language, as if you were a senior electrical engineer.

Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

RP2040 Design using only Copilot's generative AI capabilities.

Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!