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A Beginner's Guide to AI and Edge Computing Artificial Intelligence (AI) is now part of our daily lives. With companies developing low-cost AI-powered hardware into their products, it is now becoming a reality to purchase AI accelerator hardware at comparatively very low costs. One such hardware accelerator is the Hailo module which is fully compatible with the Raspberry Pi 5. The Raspberry Pi AI Kit is a cleverly designed hardware as it bundles an M.2-based Hailo-8L accelerator with the Raspberry Pi M.2 HAT+ to offer high speed inferencing on the Raspberry Pi 5. Using the Raspberry Pi AI Kit, you can build complex AI-based vision applications, running in real-time, such as object detection, pose estimation, instance segmentation, home automation, security, robotics, and many more neural network-based applications. This book is an introduction to the Raspberry Pi AI Kit, and it is aimed to provide some help to readers who are new to the kit and wanting to run some simple AI-based visual models on their Raspberry Pi 5 computers. The book is not meant to cover the detailed process of model creation and compilation, which is done on an Ubuntu computer with massive disk space and 32 GB memory. Examples of pre-trained and custom object detection are given in the book. Two fully tested and working projects are given in the book. The first project explains how a person can be detected and how an LED can be activated after the detection, and how the detection can be acknowledged by pressing an external button. The second project illustrates how a person can be detected, and how this information can be passed to a smart phone over a Wi-Fi link, as well as how the detection can be acknowledged by sending a message from the smartphone to your Raspberry Pi 5.

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TurtleBot 4 is the next-generation of the world’s most popular open source robotics platform for education and research, offering better computing power, better sensors and a world class user experience at an affordable price point.TurtleBot 4 Lite is equipped with an iRobot Create 3 mobile base, a powerful Raspberry Pi 4 running ROS 2, OAK-D spatial AI stereo camera, 2D LiDAR and more. All components have been seamlessly integrated to deliver an out-of-the-box development and learning platform.Specifications Base platform iRobot Create 3 Wheels (Diameter) 72 mm Ground Clearance 4.5 mm On-board Computer Raspberry Pi 4 (4 GB) Maximum linear velocity 0.31 m/s in safe mode0.46 m/s without safe mode Maximum angular velocity 1.90 rad/s Maximum payload 9 kg Operation time 2h 30m – 4h depending on load Charging time 2h 30m Lidar RPLIDAR A1M8 Camera OAK-D-Lite User Power VBAT @1.9 A5 V @ Low current3.3 V @ Low current USB Expansion 2x USB 2.0 (Type A)2x USB 3.0 (Type A) Programmable LEDs Create 3 Lightring Buttons and Switches 2x Create 3 User buttons1x Create 3 Power Button Battery 26 Wh Lithium Ion (14.4 V nominal) Charging Dock Included Size (L x W x H) 342 x 339 x 192 mm Weight 3.3 kg DownloadsUser Manual

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Elektor GREEN and GOLD members can download their digital edition here. Not a member yet? Click here. Small Thermal Imaging CameraAn Arduino UNO-Based DIY Solution Project Update #3: ESP32-Based Energy MeterIntegration and Testing with Home Assistant 2024: An AI OdysseyEnhancing Object Detection: Integrating Refined Techniques Raspberry Pi Goes AINew Kit Incorporates M.2 HAT+ With AI Accelerator Weather Station SensorsWhich One Should You Choose? AI-Based Water Meter Reading (1)Get Your Old Meter Onto the IoT! A GSM AlarmHarnessing GSM Technology for Remote Garage Safety Low-Power Thread Devices Optimized and ScrutinizedLow Power … Low Effort? From Life’s ExperienceThe Gender Gap DIY Cloud ChamberMaking Invisible Radiation Visible SparkFun Thing Plus MatterA Versatile Matter-Based IoT Development Board IoT RetrofittingMaking RS-232 Devices Fit for Industry 4.0 Enabling IoT with 8-Bit MCUs Technology Drives SustainabilityAdvances Lead to More Efficient Use of Energy in Many Applications AWS for Arduino and Co. (1)Using AWS IoT ExpressLink in Real Life Airflow Detector Using Arduino OnlyNo External Sensors Needed! Water Leak DetectorConnected to Arduino Cloud CrystalsPeculiar Parts, the Series Universal Garden LoggerA Step Towards AI Gardening Analog 1 kHz GeneratorSine Waves with Low Distortion Miletus: Using Web Apps OfflineSystem and Device Access Included! From 4G to 5GIs It Such an Easy Step? Starting Out in Electronics……Balances Out

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AI Projects for the Raspberry Pi with the AI HAT+ Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time—fast, private, and reliable even when the network disappears. This book is your practical guide to building exactly those kinds of systems with the Raspberry Pi AI HAT+ and the Hailo-8L accelerator. You’ll start with clear foundations: core AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge AI from cloud AI—plus an honest look at ethical considerations and future impacts. Then it’s straight to hands-on physical computing. Step by step, you’ll set up Raspberry Pi OS, power and cooling, and develop in Python using the Thonny IDE. You’ll learn GPIO basics with lights and servos, mount the AI HAT+ hardware, install and verify the Hailo software stack, and connect the right camera—official modules or USB webcams, even multiple cameras. From your first pipeline to real projects, you’ll run person detection, pose estimation, segmentation, and depth estimation, then level up with YOLO object detection: smart alerts, guest counters, and custom extensions. You’ll even connect vision to motion by combining gesture recognition with servo-driven mechanisms, including a robotic arm. With troubleshooting tips, hardware essentials, and a practical Python refresher, this book turns Edge AI from buzzword into buildable reality.

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Le téléchargement intégral de ce numéro est disponible pour nos membres GOLD et GREEN sur le site Elektor Magazine ! Pas encore membre ? Cliquez ici. le système de sécurité IA AlertAlfredBasé sur un Raspberry Pi 5 et le module Hailo 8L l'IA en développement électroniqueune mise à jour après seulement un an intro aux algorithmes de l'IAPrompt: Quels algorithmes implémentent chaque outil d'IA ? ordinateurs monocartes pour les projets d'IAAperçu et contexte des données de capteurs aux modèles d'apprentissage automatiqueDétection de gestes avec Edge Impulse et un accéléromètre créez un neurone d’intégrationet-tir avec fuiteIntelligence artificielle sans logiciel ChatGPT pour la conception électroniqueGPT-4o fait-il mieux ? intégrer l'IA périphérique avec l'ESP32-P4 fonctions vocales sur le Raspberry Pi ZeroWhen Overclocking Gives Freedom of Speech le rôle croissant de l'IA périphériqueUne tendance qui structure l'avenir exploiter la puissance de l'IA en périphérieUn entretien avec François de Rochebouët de STMicroelectronics horloge en VHDL réalisée avec ChatGPT l'impact réel de l'IASayash Kapoor à propos des "faux miracles de l'IA" et plus encore les dernières nouveautés de BeagleBoardBeagleY-AI, BeagleV-Fire, BeagleMod, BeaglePlay et BeagleConnect Freedom détection des moustiques : avec Arduino Nicla Vision et des données open source l'IA d’aujourd'hui et de demain : les idées d'Espressif, d'Arduino et de SparkFun chronologie de l'intelligence artificielle BeagleY-AIThe Latest SBC for AI Applications lumière sur l’IALes perspectives de la communauté Elektor vision artificielle avec OpenMVCréer un détecteur de canettes de soda conversation avec l'esprit numériqueChatGPT vs Gemini "Skilling Me Softley with This Bot?"L'essor de l'IA dans le secteur électronique freiné par une absence de précision sociale ?

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Build your own AI microcontroller applications from scratch The MAX78000FTHR from Maxim Integrated is a small development board based on the MAX78000 MCU. The main usage of this board is in artificial intelligence applications (AI) which generally require large amounts of processing power and memory. It marries an Arm Cortex-M4 processor with a floating-point unit (FPU), convolutional neural network (CNN) accelerator, and RISC-V core into a single device. It is designed for ultra-low power consumption, making it ideal for many portable AI-based applications. This book is project-based and aims to teach the basic features of the MAX78000FTHR. It demonstrates how it can be used in various classical and AI-based projects. Each project is described in detail and complete program listings are provided. Readers should be able to use the projects as they are, or modify them to suit their applications. This book covers the following features of the MAX78000FTHR microcontroller development board: Onboard LEDs and buttons External LEDs and buttons Using analog-to-digital converters I²C projects SPI projects UART projects External interrupts and timer interrupts Using the onboard microphone Using the onboard camera Convolutional Neural Network

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Temporary Delay in the Delivery of Unitree Robots Like many other suppliers, we are currently experiencing delays in the delivery of Unitree robots. A shipment from our supplier is currently held in customs, which has unfortunately led to later-than-planned deliveries for previously placed orders. We are actively working with our supplier to resolve this issue and expect more clarity soon, but at this time, we cannot provide any guarantees. Additionally, a new shipment is already on its way, though it will take some time to arrive. Since other suppliers are facing similar challenges, switching to a different provider is unlikely to result in a faster solution. Our top priority remains fulfilling existing orders. If you have any questions or would like to update your order, please do not hesitate to contact our customer service team. We will keep you informed of any further developments. Unitree Go2 series consists of quadruped robots for the research & development of autonomous systems in the fields of human-robot interaction (HRI), SLAM & transportation. Due to the four legs, as well as the 12DOF, this robot can handle a variety of different terrains. The Go2 comes with a perfected drive & power management system, which enables a speed (depending on the version) of up to 3.7 m/s or 11.88 km/h with an operating time of up to 4 hours. Furthermore, the motors have a torque of 45 N.m at the body/thighs and at the knees, which also allow jumps or backflips. Features Super Recognition System: 4D LIDAR L1 Max Running Speed: approx. 5 m/s Peak Joint Torque: approx. 45 N.m Wireless Module: WiFi 6/Bluetooth/4G Ultra-long battery Endurance: approx. 2-4 h (long battery life measured in real life) Intelligent Side-follow System: ISS 2.0 Specifications Tracking module: Remote-controlled or automatic tracking Front camera: Image tansmission Resolution 1280x720, FOV 120°, Ultra wide angle lens deliver rich clarity Front lamp: Brightly lights the way ahead 4D LiDAR L1: 360°x90° omnidirectional ultra-wide-angle scanning allows automatic avoidance with small blind spot and stable operation 12 knee joint motors: Strong and powerful, Beautiful and simple, Brandy new visual experience Intercom microphone: Effective communication with no scenario restrictions Self-retracting strap: Easy to carry and load things More stable, more powerful with advanced devices: 3D LiDAR, 4G ESIM Card, WiFi 6 with Dual-band, Bluetooth 5.2 for stable connection and remote control Powerful Computing Core: Motion controller, High-performance ARM processor, Improved Al algorithm processor, External ORIN NX/NANO Smart battery: Standard 8000 mAh battery, Long-endurance 15000 mAh battery, Protection from over-temp, overcharge and short-circuit Speaker for music play: Listen to music as your pleasure Unitree Go2 Variants The Go2 impresses not only with its technical capabilities, but also with a modern and slim design that gives it a futuristic look and makes it a real eye-catcher. The Go2 Air is specially designed for demos and presentations. With its basic features, it offers a solid basis for demonstrating the movement capabilities and functionality of a four-legged robot. Important: The Go2 Air is delivered without a controller. This can be purchased optionally. With a powerful 8-core high-performance CPU, the Pro and Edu offer impressive computing power required for complex tasks and demanding calculations. This enables faster and more efficient data processing and makes the Pro and Edu a reliable partner for your projects. From the Edu version onwards, the Go2 is programmable and opens up endless possibilities for developing and researching your own robotics applications. The Go2 is also able to handle a step height of up to 14 cm. This makes it an ideal tool for research, education and entry into the world of robotics. The Go2 Edu comes with a remote controller that gives you easy and intuitive control. You also get a docking station with impressive computing power of 100 TOPS, which is equipped with powerful AI algorithms and offers you technical support. Go2 Edu is equipped with a powerful 15000 mAh battery that gives it an impressive runtime of up to 4 hours. This long operating time allows the robot to carry out longer exploration missions and complete demanding tasks. Model Comparison Air Pro Edu/Edu Plus Dimensions (standing) 70 x 31 x 40 cm 70 x 31 x 40 cm 70 x 31 x 40 cm Dimensions (crouching) 76 x 31 x 20 cm 76 x 31 x 20 cm 76 x 31 x 20 cm Material Aluminium alloy + High strength engineering plastic Aluminium alloy + High strength engineering plastic Aluminium alloy + High strength engineering plastic Weight (with battery) about 15 kg about 15 kg about 15 kg Voltage 28~33.6 V 28~33.6 V 28~33.6 V Peaking capacity about 3000 W about 3000 W about 3000 W Payload ≈7 kg (MAX ~ 10 kg) ≈8 kg (MAX ~ 10 kg) ≈8 kg (MAX ~ 12 kg) Speed 0~2.5 m/s 0~3.5 m/s 0~3.7 m/s (MAX ~ 5 m/s) Max Climb Drop Height about 15 cm about 16 cm about 16 cm Max Climb Angle 30° 40° 40° Basic Computing Power N/A 8-core High-performance CPU 8-core High-performance CPU Aluminum knee joint motor 12 set 12 set 12 set Intra-joint circuit (knee) ✓ ✓ ✓ Joint Heat Pipe Cooler ✓ ✓ ✓ Range of Motion Body: −48~48° Body: −48~48° Body: −48~48° Thigh: −200°~90° Thigh: −200°~90° Thigh: −200°~90° Shank: −156°~−48° Shank: −156°~−48° Shank: −156°~−48° Max Torque N/A about 45 N.m about 45 N.m Super-wide-angle 3D LiDAR ✓ ✓ ✓ Wireless Vector Positioning Tracking Module N/A ✓ ✓ HD Wide-angle Camera ✓ ✓ ✓ Foot-end force sensor N/A N/A ✓ Basic Action ✓ ✓ ✓ Auto-scaling strap N/A ✓ N/A Upgraded Intelligent OTA ✓ ✓ ✓ RTT 2.0 Image Transmission ✓ ✓ ✓ App Basic Remote Control ✓ ✓ ✓ App Data Viewing ✓ ✓ ✓ App Graphical Programme ✓ ✓ ✓ Front Lamp (3 W) ✓ ✓ ✓ WiFi 6 with Dual-band ✓ ✓ ✓ Bluetooth 5.2/4.2/2.1 ✓ ✓ ✓ 4G Module N/A CN/GB CN/GB Voice Function N/A ✓ ✓ Music Playback N/A ✓ ✓ ISS 2.0 Intelligent side-follow system N/A ✓ ✓ Intelligent detection and avoidance ✓ ✓ ✓ Secondary development N/A N/A ✓ Manual controller Optional Optional ✓ High computing power module N/A N/A Edu: 40 TOPS computing power Edu Plus: 100 TOPS computing power NVIDIA Jetson Orin (optional) Smart Battery Standard (8000 mAh) Standard (8000 mAh) Long endurance (15000 mAh) Battery Life 1-2 h 1-2 h 2-4 h Charger Standard (33.6 V, 3.5 A) Standard (33.6 V, 3.5 A) Fast charge (33.6 V, 9 A) Included 1x Unitree Go2 Pro 1x Unitree Go2 Battery (8000 mAh) Downloads Documentation iOS/Android apps GitHub

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Learn to Build Intelligent Embedded Systems Build smarter embedded systems with Arduino UNO Q. This book gives you the tools, knowledge, and confidence to turn ideas into intelligent, working solutions using the Arduino UNO Q platform. Discover how to build intelligent embedded systems with the Arduino UNO Q and AI. Unlock the full potential of the Arduino UNO Q, a next-generation platform that combines the real-time power of the STM32U585 microcontroller with the flexibility of a Qualcomm Dragonwing QRB2210 microprocessor. Learn how to rapidly prototype real-world applications using the Arduino IDE for low-level embedded control and Python in Arduino App Lab for high-level development. Build confidence through hands-on projects that guide you step by step from basic board features to complete working systems. Explore ready-to-use, AI based Arduino App Lab examples and see how they can jump-start your development and reduce time to deployment. Step into the world of Edge AI with a clear, practical introduction to Edge Impulse Studio—no prior AI experience required. Follow a complete, real-world workflow to create a Keyword Spotting AI application, covering data collection, model training, optimization, and on-device inference using the Edge Impulse Studio. Bridge the gap between embedded systems and machine learning and learn how to bring intelligence directly onto your hardware. Perfect for embedded engineers, educators, students, and makers looking to stay ahead in AI-enabled product development.

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Diese Ausgabe steht allen GOLD- und GREEN-Mitgliedern auf der ElektorMagazine-Website zum Download bereit! Sie sind noch kein Mitglied? Hier klicken! Die Open-Source-Prozessorarchitektur RISC-V16 Boards und MCUs, die Sie kennen sollten Audio-Player mit FPGA und EqualizerTeil 1. Digitale Audiosignale mischen mit einem MKR Vidor 4000 von Arduino Laserkopf für Pico-basierte SanduhrZeichnen mit Licht Nehmen Sie teil am STM32 Edge AI Contest! Umweltüberwachung von Pflanzen mit mehreren SensorenDrahtlose Messung der Wasserversorgung und Lichtverhältnisse Maixduino: Automatischer KI-TüröffnerGesichtserkennung mit einer Kamera Embedded-Elektronik 2024KI wird die Branche neu definieren Ladungsbasiertes In-Memory Compute bei EnCharge AI KI-Inferencing bei 10-fach geringerer Leistungsaufnahme zu 20-fach geringeren Kosten Click-Board hilft bei der Entwicklung und dem Training von ML-Modellen für die Schwingungsanalyse Der Elektor Mini-WheelieEin Bausatz für einen selbstbalancierenden Roboter MCU, ich sehe dich!MCUViewer, ein Open-Source-Multiplattform-Debugging-Werkzeug USB-2.0-IsolatorGeräte galvanisch isoliert per USB verbinden Eingriff vor dem SchadenPredictive Maintenance in der Praxis SPoE - Elektromagnetische VerträglichkeitSingle-Pair mit Power-over-Ethernet im EMV-Fokus Retro-TechnikWie das Farbfernsehen eine neue Welt schuf EKG-MonitorMit Hexabitz-Modulen und einem STM32CubeMonitor Der Kampf um KI at the Edge HaLow erreicht Rekorddistanz von 16 km für WLAN bei 900 MHz Erster CHERI RISC-V-Embedded Chip und Early Access Programm Die dritte Generation der Waldbrandfrüherkennung nutzt Satellitenverbindungen Aus dem Leben gegriffenDie Qual der Wahl Aller Anfang......ist nicht schwer: Wir setzen die Filterung und Klangregelung fort. Riesen-LED-UhrNeuauflage eines Elektor-Klassikers Ein modularer Ansatz für die SensorprüfungDas Sensor Evaluation Board mit ESP32-S3 2025: Eine Odyssee in die KIDer Aufstieg der Basismodelle und ihre Rolle bei der Demokratisierung der KImocratizing AI Standalone MIDI-Synthesizer mit Raspberry PTeil 1: Eine Plattform für Edge-KI-Experimente vorbereiten Projekt 2.0Korrekturen, Updates und Leserbriefe CPU, GPU, DSP, FPGA; ein universeller KI-RISC-V-Prozessor kann alles! CEO-Interview: Ventivas dünne und kühle Technologie Dual-Core-Programmierung beim Raspberry Pi PicoEinstieg in die Welt der parallelen Programmierung

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Diese Ausgabe steht allen GOLD- und GREEN-Mitgliedern auf der ElektorMagazine-Website zum Download bereit! Sie sind noch kein Mitglied? Hier klicken! Der ESP-BLASTEine Mini-Raketendrohne mit ESP32, die über 100 km/h erreicht Wireless Workshop (1)Freigeschaltetes Funkmodul: 433-MHz-CC1101-Sub-GHz-Transceiver AM-Sendermit Rechteck-/Sinuswellengenerator auf Raspberry Pi Pico STM32Cube Innovations ChallengeTreten Sie mit Ihrem besten STM32C5-Design an CoffeeCallerWie ein langer Flur zu einer maßgefertigten Mesh-Netzwerk-Platine führte AudiotronicsKlirrfaktor Elektor ist 65Projekte aus früheren September/Oktober-Ausgaben Geräteortung durch Bluetooth Channel SoundingZentimetergenaue Entfernungsmessung im IoT der nächsten Generation 2026: Eine KI-OdysseeQuantencomputing ist nicht die nächste GPU Cloud-Schutz durch Edge-Sicherheit Erste Erfahrungen mit dem HackRF ProPraktische Tests mit der neuen SDR-Hardware Herausforderung ElektromobilitätEffiziente Lötlösungen für moderne Fahrzeugtechnik Modem-Update von GPRS/2G auf LTE/4Gam Beispiel einer Funk-Alarmzentrale Loop-Antenne mit RF BricksTeil 1: Kurzwellen-Antenne mit einstellbarer Richtwirkung im Selbstbau Internetradio-zu-AM-KonverterVerwendung eines ESP32-S3 als Sender Projekt 2.0 mioty – ein ÜberblickLPWAN à la Fraunhofer Werkbank-WeisheitPraktische Tipps von Experten Walter, das kompakte IoT-ModulTeil 2: Zugriff auf Wetter- und Datendienste

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Elektor GREEN and GOLD members can download their digital edition here. Not a member yet? Click here. The ESP-BLASTA Mini ESP32 Rocket Drone That Flies 100 km/h+ Wireless Workshop (1)Radio Modules Unlocked: 433 MHz CC1101 Sub-GHz Transceiver AM TransmitterWith Square/Sine Wave Generator on Raspberry Pi Pico STM32Cube Innovation ChallengeCompete with Your Best STM32C5 Design CoffeeCallerHow a Long Hallway Led to a Custom Mesh-Networked PCB AudiotronicsTotal Harmonic Distortion Elektor at 65Projects from Septembers and Octobers Past Bluetooth Channel Sounding for Asset TrackingCentimeter-Level Precision Ranging for Next-Generation IoT 2026: An AI OdysseyQuantum Computing Is Not the Next GPU Protecting the Cloud Demands Security at the Edge First Experiences with the HackRF ProPractical Tests with the New SDR Hardware Modem Upgrade from GPRS/2G to LTE/4GUsing a Wireless Alarm Control Panel as an Example Loop Antenna with RF BricksPart 1: A DIY Shortwave Antenna with Adjustable Directivity Internet Radio to AM ConverterUsing an ESP32-S3 as Transmitter Err-lectronics mioty — An OverviewFraunhofer’s Approach to LPWAN Workbench WisdomPractical Tips from Experts Walter, the Compact IoT ModulePart 2: Accessing Weather and Data Services

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Le téléchargement intégral de ce numéro est disponible pour nos membres GOLD et GREEN sur le site Elektor Magazine ! Pas encore membre ? Cliquez ici. L'architecture de processeur open-source RISC-V16 cartes et MCU à connaître un lecteur audio avec égaliseur basé sur un FPGAMixage audio numérique avec un Arduino MKR Vidor 4000 tête laser pour l’horloge de sable basé sur Raspberry Pi PicoDessiner avec la lumière participez au concours STM32 Edge AI système de contrôle environnemental multi-capteurs pour les plantesMesure sans fil de l'approvisionnement en eau et de la luminosité porte automatique contrôlée par l'IA et MaixduinoReconnaissance faciale avec une caméra l’électronique embarquée en 2024L’IA va redéfinir l’industrie Calcul en mémoire basé sur la charge chez EnCharge AI des opérations d'IA avec 10 fois moins d'énergie et des coûts divisés par 20 Une carte pour le développement et l’entraînement des modèles ML d’analyse des vibrations Elektor Mini-WheelieKit robot gyropode (robot autostabilisé) MCUViewerMCUViewer outil de débogage open source multiplateforme isolateur USB 2.0Isolation éléctrique pour les périphériques USB anticipation et actionApplication pratique de la maintenance prédictive SPoE – compatibilité électromagnétiquePaire unique avec Power-over-Ethernet à travers les yeux d'EMC rétro-techCréer un monde nouveau avec la télévision couleur surveillance ECGavec des modules Hexabitz et STM32CubeMonitor la bataille pour l’IA en périphérie HaLow atteint une distance Wifi record de 16 km à 900 MHz première puce embarquée CHERI RISC-V et programme d'accès anticipé la détection des incendies de forêt de troisième génération utilise des liaisons satellites sur le vifDélices et supplices du choix démarrer en électronique......Filtrage et contrôle de la tonalité Kit d'horloge quasi-analogiqueNouvelle version d'un classique d'Elektor une approche modulaire de test des capteursCarte de test de capteurs basée sur l'ESP32-S3 2025 : une odyssée de l'IAL'essor des modèles de fondation et leur impact sur l'accessibilité de l'IA Synthétiseur MIDI autonome Raspberry Pi (1)Préparation d'une plateforme pour des expériences d’IA en périphérie projet 2.0Corrections, mises à jour et courrier des lecteurs Le RISC-V AI, un processeur à tout faire : CPU, GPU, DSP, FPGA paroles de PDG : fraîcheur, silence et finesse programmation Dual-Core avec le Raspberry Pi PicoLe monde de la programmation parallèle

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