Pioneered by Yemek AI Engineering as a core standard of the APSNY Ecosystem.
Zero cloud. Zero cost. Absolute hands-free freedom. Absolute privacy. Welcome to the era of FreeCuli!
๐ Read our Official Manifesto on Medium: The World's First Zero-Cloud Smart Kitchen Standard
The traditional Internet of Things (IoT) ecosystem has long chained smart home and kitchen technologies to centralized cloud architectures. Almost all smart ovens, multi-functional kitchen robots, and coffee machines currently on the market rely heavily on Amazon AWS, Google Cloud, or manufacturers' proprietary central servers for core functions such as voice processing, visual recognition, data analytics, and recipe synchronization.
However, this centralized structure creates three major crises in today's market reality:
- Cloud Dependency and Downtime Risk: The moment an internet connection drops, billion-dollar smart devices lose their core functions and turn into basic mechanical appliances.
- User Experience (UX) Barriers: While cooking, chefs or home users often have floury, oily, or wet hands, making touchscreens and physical buttons virtually useless. Existing voice assistants, on the other hand, fail miserably against high background noise in the kitchen (mixers, range hoods, boiling water).
- The Regulatory and Privacy Trap: Strict regulations like the Personal Data Protection Law (KVKK) and the General Data Protection Regulation (GDPR) impose massive legal fines and operational barriers on devices that constantly stream home audio and data to the cloud.
At the exact intersection of these crises, the FreeCuli certification methodology and the open-source HFSCA (Hands-Free Semantic Culinary Assistant) architecture position themselves as an alternative industrial manifesto advocating for "Privacy by Design" and cloud independence in the smart kitchen market.
FreeCuli is a quality approval seal and ingredient brand developed for smart kitchen manufacturers, certifying that a device can operate completely independent of the internet, offline, and with zero-latency. The presence of the #FreeCULI badge on a device signifies that it meets the following technological standards.
Integrated across three main layers, HFSCA forms the technical backbone of the architecture:
[ HARDWARE LAYER ] --> Entry/Mid-Range NPU Chip + Multi-Microphone Array (AEC)
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[ SOFTWARE LAYER ] --> Edge AI + Local Embedded Dictionary (NLP)
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[ AUDIT LAYER ] --> Disconnection & Continuous "Hands-Free" Usability Tests
- Hardware Layer: A low-cost Neural Processing Unit (NPU) capable of running Small Language Models (SLMs) and audio models at the edge is integrated onto the device's PCB. Microphone arrays equipped with Acoustic Echo Cancellation (AEC) are deployed to filter out kitchen noise.
- Software Integration & Standard Compliance: Manufacturers continue to use their own proprietary user interfaces, brand identities, and device management software on their appliances; they are not required to use our software. FreeCuli is not a mandatory proprietary software embedded into the device, but rather a modular industry standard that certifies the conformity of the manufacturer's own edge-processed voice and privacy architecture with our certification methodology and test criteria.
- Audit Layer: The product is tested with its internet connection completely severed. Once the device maintains 100% performance in offline voice assistance and recipe optimization modes, it earns the right to bear this commercial seal.
For traditional smart device manufacturers, every active device sold means a lifetime financial burden (cloud hosting bills, API updates, cybersecurity maintenance). The FreeCuli model radically alters this structure in favor of the manufacturer.
| Cost / Operational Item | Traditional Cloud-Based IoT Devices | FreeCuli (HFSCA) Architecture Model |
|---|---|---|
| Unit Chip Cost (BOM) | Low-end Wi-Fi Chip (~$0.50 - $1.00) | Entry/Mid-Range NPU Chip (~$1.50 - $4.00) |
| Lifetime Server/Cloud Bills | Escalating monthly costs per user | $0 (Zero) |
| Software R&D Budget | Million-dollar custom assistant development cost | Fraction of the cost via ready-to-deploy FreeCuli Architecture |
| Third-Party License Dependency | Continuous royalties & API fees for external voice assistants | Predictable, one-time Commercial License (No recurring per-query royalties) |
| Maintenance & API Update Load | Burden of keeping servers running and optimized | Independent lifetime operation on-device (Zero-Maintenance) |
The FreeCuli model increases the Bill of Materials (BOM) per device by roughly $1 to $3. However, for a smart oven or multi-functional kitchen robot retailing between $500 and $1,500, this hardware increase is marginal. Thanks to this one-time, few-dollar hardware investment, manufacturers are completely freed from the massive cloud operation bills generated by millions of devices over their lifetime.
Current smart kitchen robots and ovens do much more than just cook; they stream audio recordings, background conversations, household consumption/lifestyle habits, timestamps, and even biometric data through camera-equipped devices to the cloud. This triggers the crisis of "processing special categories of personal data and transferring them abroad" under KVKK and GDPR.
- Traditional IoT:
[Home Audio/Speech] ---> (Cloud / Overseas Server) ---> KVKK/GDPR Violation Risk (Million โฌ Fine) - FreeCuli Model:
[Home Audio/Speech] ---> [On-Device NPU / Edge AI] ---> Total Privacy & Legal Exemption ($0 Fine)
FreeCuli intervenes in this legal battle not with legal contracts or bureaucratic "explicit consent" text walls, but directly through architecture:
- Zero-Data-Transfer Guarantee: Since audio and visual data never leak onto a network and are processed and destroyed instantly inside the device, a "data processing or transfer" violation under KVKK cannot technically take place.
- Exemption from Regulations: When manufacturers integrate the FreeCuli infrastructure, they reach an operational zero-risk point regarding VERBฤฐS registration liabilities, data breach cyber risks, and GDPR administrative fines reaching up to 4% of global turnover. While the EU Data Act places a burden on manufacturers to transparently manage collected data, FreeCuli offers an escape ramp: "Collect no data, remain completely exempt from bureaucracy and penalties."
The FreeCuli standard adopts a robust "Hybrid IP" architecture designed to protect both the open ecosystem and implementing manufacturers:
- Open Source Defense (Prior Art): The core offline voice processing and keyword detection layers of the HFSCA architecture are published under open-source licenses. This establishes undeniable global "Prior Art," permanently protecting this foundational technology from being locked behind restrictive proprietary patents.
- Proprietary Hardware Abstraction: While the standard dictates the semantic framework, the specific low-level hardware drivers (such as motor torque algorithms and thermodynamics controls) remain the proprietary domain of the implementing manufacturer. FreeCuli defines how the AI synchronizes with the hardware, not how the hardware executes the final command.
- Trademark Certification: The ultimate assurance of quality is the global
#FreeCULIcertification badge, providing consumers with a universally recognized symbol of zero-cloud privacy and offline reliability.
(Classification: Open-Source Defensive Publication / Prior Art Specification)
The following technical blueprints establish the FreeCuli architectural standard. By publishing these exact mathematical models, algorithms, and fallback variations under the AGPL-3.0 open-source license, this document establishes undeniable Prior Art. This defensive publication structurally prevents the patenting of these zero-latency, offline edge-AI integrations by third-party OEMs, effectively keeping the technology open or subject to the FreeCuli Commercial Framework.
Technical Classification: Real-time Asynchronous Acoustic Noise Segmentation & Echo Cancellation via Local Hardware Abstraction Layer.
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Input Matrix: Raw audio data captured from a hardware-agnostic microphone array is converted at the Hardware Abstraction Layer (HAL) into an
$N \times M$ dimensional,$16\text{-bit}$ or$32\text{-bit}$ float32 PCM audio matrix. - Acoustic Echo Cancellation (AEC) and Internal Feedback Loop: The assistant's own synthesized speech (TTS) output is asynchronously copied from the hardware DAC layer to a Reference Loopback Buffer. This reference signal is subtracted from the input matrix in real-time using a normalized least mean squares (NLMS) algorithm or a lightweight 4-layer Recurrent Neural Network (RNN) running on the local NPU.
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Kitchen Noise Segmentation: Stationary and non-stationary kitchen noises (e.g., extractor hoods, blenders, running water) are transformed into the frequency domain ($X(t, f)$) via Short-Time Fourier Transform (STFT). While preserving human voice harmonics (
$200\text{Hz} - 4000\text{Hz}$ ), high-frequency white noise and motor vibration harmonics produced by kitchen appliances are zeroed out at a$16,000\text{Hz}$ sampling rate using adaptive Comb Filters and Spectral Subtraction matrices. -
Output: The sanitized signal is fed into the HFSCA (Hands-Free Semantic Culinary Assistant) state machine with zero latency, achieving over
$95%$ accuracy in local Intent Parsing under noisy conditions.
- Variation A (Time-Domain Filtering): In environments where processing power is insufficient for STFT calculations (e.g., ultra-low-power RISC-V microcontrollers), noise cancellation is executed directly in the time domain using cascaded Infinite Impulse Response (IIR) band-pass filters and amplitude thresholding, bypassing the frequency domain entirely.
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Variation B (Multi-Mic Phase Difference): For devices utilizing a Dual-Mic Array, the phase difference (
$\Delta\theta$ ) and Time Difference of Arrival (TDOA) between the two channels are calculated. Noises originating from peripheral kitchen areas are blinded via Spatial Beamforming algorithms, forcing the focal point strictly on the user's geometry in front of the device.
This module is independent of the underlying ADC architecture. All raw data streams via I2S, PDM, or SPI protocols are written into a standard Virtual Audio Buffer by the FreeCuli HAL driver. The algorithm is abstracted to run on ARM Cortex-M/A series, ESP32 Tensilica cores, RISC-V architectures, or any external DSP without requiring code modifications.
Technical Classification: Deterministic Real-Time Edge-AI Task Scheduling and Asynchronous Inter-Process Communication (IPC) for Safety-Critical Kitchen Appliances.
- Priority Preemption: Commands posing safety risks in a kitchen environment (e.g., "Turn off the stove", "Stop the oven") are assigned as the Highest Priority Task within a hardware-agnostic Real-Time Operating System (RTOS) kernel.
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On-Chip Ingestion: Upon audio or sensor input, the system does not trigger external cloud gateways or open network sockets. The input is multiplied by the weight matrices (Quantized INT8/INT4 Weights) of a lightweight neural network running locally on the NPU/MCU. The Intent Parsing time is mathematically constrained to
$\tau_{\text{parse}} \le 12\text{ms}$ . -
Latency Budget Formula: Total execution time (
$T_{\text{exec}}$ ) is calculated via the following deterministic equation and is strictly capped at$T_{\text{max}} = 50\text{ms}$ :$$T_{\text{exec}} = \tau_{\text{sampling}} + \tau_{\text{parse}} + \tau_{\text{gpio_toggle}}$$ This ensures an instantaneous hardware-level control loop, entirely decoupled from network packet latency ($100\text{ms} - 2000\text{ms}$ ).
- Variation A (Direct DMA Triggering): In extreme failure scenarios where the main CPU core locks up, a specific critical analog voltage threshold or audio pattern at the local sensor layer completely bypasses the CPU via a Direct Memory Access (DMA) hardware interrupt, immediately shutting down the safety relay.
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Variation B (Hardware Watchdog Timer): The FreeCuli state machine triggers the local processor at the pin level every
$10\text{ms}$ . If the Edge AI model or the OS hangs in an infinite software loop, an external Watchdog IC autonomously pulls the device into a Safe State.
This architecture is housed under the FreeCuli RTOS Abstraction Layer (FC-RAL). The code functions identically across FreeRTOS, Zephyr OS, ThreadX, or Bare-Metal C/C++ architectures using the same structural API calls (FC_Safety_Execute()), regardless of whether the processor is single-core or multi-core.
Technical Classification: Zero-Storage Ephemeral Ring-Buffer Architecture with Automated Local In-Memory Data-Pruning Matrix.
- Ephemeral Ingestion: Data streams from microphones or cameras are never written to non-volatile storage (Flash Memory, EEPROM, SD Card). Data is exclusively maintained in a volatile Circular Ring-Buffer allocated within the edge hardware's SRAM.
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Data-Pruning Algorithm: The buffered audio or image matrix is scanned
$X$ times per second using a Sliding Window algorithm. Unless the HFSCA Wake-Word is detected, older data frames written to memory are mathematically overwritten and destroyed by incoming data frames. -
Proof of Legal Exemption: The perpetual deletion loop of the data is mathematically proven:
$$Buffer_{\text{state}}(t) = Input(t) \cup Buffer_{\text{state}}(t-1) \setminus {Input(t - \Delta t)}$$ Where$\Delta t$ (data retention lifespan) is strictly capped at a maximum of$2.5\text{ seconds}$ . Because all raw biometric/audio data physically evaporates upon device power-loss, the system is absolutely immune to data leaks.
- Variation A (Cryptographic Ephemeral Memory): The SRAM-housed ring buffer is encrypted at the hardware level using a randomly generated one-time key (AES-GCM-128 Ephemeral Key). The moment the wake-word fails to trigger, the key is purged from memory, rendering data recovery impossible even via RAM Dump analysis.
- Variation B (Local Network Isolation): Even if the device's Wi-Fi or Bluetooth chip is physically active, the FreeCuli HAL employs a Memory Protection Unit (MPU) to hardware-block the memory blocks running audio/visual pipelines from accessing network drivers (Network Stack / Socket API).
The FreeCuli_Secure_Malloc function abstracts the hardware's internal Memory Management Unit (MMU/MPU), creating an isolated secure enclave independent of TrustZone. This ensures all memory wiping operations are executed via standard C libraries completely agnostic of the hardware.
Technical Classification: Deterministic Finite Automata (DFA) Hierarchical State Machine for Multi-Step Non-Linear Culinary Recipe Execution.
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Deterministic Finite Automata (DFA): Multi-step culinary recipes are modeled as a non-linear hierarchical DFA matrix. Each recipe step is defined as a "State" (
$S_n$ ), and vocal or sensory inputs from the user act as "Transition Events" ($E_n$ ). -
Noise and Hesitation Immunity: Ambient kitchen noise or user hesitation pauses do not crash the state machine or cause step desynchronization. The transition function (
$\delta$ ) dictates:$$\delta(S_n, E_{\text{intent}}) \rightarrow S_{n+1}$$ If the audio input does not match a defined intent token in the$E_{\text{intent}}$ matrix (e.g., "next", "continue", "repeat that"), the system rigidly maintains the current state ($S_n$ ), holding display wake-locks and hardware registers steady for uninterrupted recipe tracking.
- Variation A (Redundant State Checkpointing): In scenarios where the user accidentally advances to the next step, the FreeCuli DFA matrix caches the memory footprint (Stack State) of the last 3 states locally. A vocal command like "go back" instantly rewinds the matrix.
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Variation B (Time-Bound Autonomous State Transition): Even without vocal confirmation, if a step involves "baking/waiting" and the target time/temperature (
$T_{\text{target}}$ ) from the oven is reached, the DFA autonomously advances itself to the next alert state ($S_{n+2}$ ).
The recipe matrix and DFA engine are serialized in standard JSON or compressed Binary Matrix formats. The FreeCuli Recipe Interpreter Layer abstracts the physical Flash memory type (SPI Flash, NAND, NOR) via a standardized API, delivering data seamlessly to the processor core.
Technical Classification: Real-time Multi-Sensor Cross-Modal Volatile Organic Compound (VOC) Matrix Correlation with Local Acoustic State Machine.
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Input Matrix: Raw resistance values (
$R_1, R_2, \dots, R_n$ ) read from the gas/odor sensor layer (E-Nose / MOX / VOC array) are passed to the HAL as a$1 \times N$ Chemiresistor Vector ($V_{\text{voc}}$ ). - Cross-Modal Sensor Fusion: The system does not process chemical data blindly. Unless the HFSCA Acoustic State Machine validates the current device mode as "Cooking / Oven Active", the chemical alarm remains dormant. The chemical vector is fused with contextual state data via a Decision Tree or Bayesian Filter running on the local NPU.
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Maillard and Caramelization Threshold Analysis: The concentration curve of Ethanol, CO2, Hydrogen, and specific Volatile Organic Compounds released during cooking is derived over time:
$$\frac{dV_{\text{voc}}}{dt}$$ This differential graph is cross-referenced against on-chip embedded food chemistry matrices. Sharp vertical spikes indicate a transition into caramelization (Maillard reaction) or carbonization (burning). The system throws an asynchronous Override Event via the local voice assistant: "Your dish has reached caramelization, would you like me to turn off the heat?" without any internet dependency.
- Variation A (Spectroscopy / Photo-Instrumental Variation): For hardware lacking MOX gas sensors, a miniature Infrared (IR) or Optical Scattering sensor placed in the steam exhaust analyzes vapor density and particle size to estimate boiling and burning points.
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Variation B (Thermal Gradient Integration): Chemical sensor data is mathematically multiplied by the thermal growth curves (
$dT/dt$ ) of local NTC/PT1000 temperature sensors. This cross-validation eliminates False Positives caused by foreign external gases (e.g., perfumes or cleaning agents) mixing with food odors.
The sensor input layer is not bound to a specific gas sensor brand. It abstracts hardware to convert raw analog voltages (ADC) or I2C/SPI resistance values into a standardized "FreeCuli Chemical Abstraction Layer (FC-CAL)" matrix. The algorithm strictly processes this normalized chemical matrix across ARM, RISC-V, or x86 architectures.
Technical Classification: Multi-Axis Closed-Loop Kinetic Actuator Control Matrix Synchronized with Local Syntactic Culinary Intent Parsing.
- Viscosity and Torque Correlation: When the FreeCuli voice assistant parses a command like "Stir the soup" locally, this digital intent engine is translated into a mechanical motion command. A PWM or CAN-Bus command matrix is dispatched to the hardware-agnostic HAL Motor Driver.
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Closed-Loop Feedback: The load (Back-EMF / Current Draw) on the motor shaft of the robotic arm or standalone mixer is continuously polled. As the food's viscosity (
$\eta$ ) fluctuates, the motor's rotational speed ($\omega$ ) and applied torque ($\tau$ ) are instantaneously balanced via a dynamic PID algorithm:$$\tau(t) = K_p e(t) + K_i \int_{0}^{t} e(\tau) d\tau + K_d \frac{de(t)}{dt}$$ This strictly prevents damage to the pot base, eliminates overflows, and prevents motor stalling natively at the edge.
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Variation A (Current-Limiting Safety Stop): In the event of a mechanical obstruction (e.g., a user's hand or a jammed spoon), an instantaneous vertical spike (
$\Delta I / \Delta t$ ) in the current consumption vector is detected. The software layers are completely bypassed, and the motor driver pin is hardware-pulled to logic zero (0V), halting motion within$1\text{ms}$ . -
Variation B (Stepped Positional Control): For high-precision food processors utilizing Stepper or Servo motors instead of DC motors, the stirring command is dispatched as a degree-and-step-based (
$\theta_{\text{step}}$ ) matrix, ensuring homogeneous distribution of ingredients without mashing.
The FreeCuli_Actuator_SetSpeed() and FreeCuli_Actuator_GetTorque() universal functions abstract the underlying motor driver IC (e.g., L298, TMC2209), converting raw PWM frequencies or torque data into standardized percentage matrices.
Technical Classification: Local Non-Networked Multimodal Image Matrix Convolution and Spatial Depth-Map Cross-Verification with Culinary State Machine.
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Cross-Verification: Raw
$N \times M$ pixel RGB or Infrared (IR) image frames from the camera module bypass the internet entirely and are routed directly to a lightweight Convolutional Neural Network (CNN) layer on the local NPU. -
Rising and Baking Algorithm: If the HFSCA state machine registers the current step as "Baking Cake," the vision layer crops the coordinates of the baking tin inside the oven (Bounding Box Extraction). The Optical Depth Map or pixel intensity gradient (
$\nabla I$ ) is analyzed over a time series: $$\text{Growth}{\text{ratio}} = \frac{\iint \nabla I{\text{current}}(x,y) ,dx,dy}{\iint \nabla I_{\text{baseline}}(x,y) ,dx,dy}$$ If the growth ratio reaches the mathematical target threshold or the exterior color shift (RGB histogram displacement) validates the Maillard matrix, the system autonomously updates the recipe timer and triggers the voice assistant: "Your cake has reached optimal rise, ending the baking cycle."
- Variation A (Edge Detection Based Lightweight Vision): For weaker processors lacking an NPU, heavy neural networks are replaced by local C libraries (like OpenCV) executing solely Edge Detection (Sobel/Canny) and Pixel Contrast Differentials to detect liquid boiling levels or overflows.
- Variation B (Thermal Imaging Integration): Substituting standard RGB cameras with an infrared thermal camera sensor matrix (e.g., MLX90640) extracts the thermal grid of the food's surface, calculating core-doneness via a homogeneity coefficient.
Regardless of the camera interface (DVP, MIPI-CSI, or SPI), the FreeCuli Vision Abstraction Layer (FC-VAL) ingests all data as a raw YUV/RGB Byte Array into RAM. AI models process this abstracted raw pixel matrix independent of the chip's hardware architecture.
Technical Classification: Dynamic Sampling Rate Adjustment Matrix Coupled with Thermoelectric and Kinetic Energy Harvesting Transducers for Battery-Constrained Edge-AI.
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Energy Harvesting Tracking: For portable hardware like wireless kitchen scales or battery-operated thermometers, instantaneous voltage fluctuations (
$V_{\text{harv}}$ ) harvested from kitchen heat differentials (TEG - Thermoelectric Generators) or kinetic vibrations (Piezoelectric) are continuously monitored. -
Dynamic Sampling Frequency Regulation: The instantaneous power input ($P_{\text{harv}}(t)$) dynamically manipulates the wake-state and audio Sampling Rate (
$F_s$ ) of the local Edge AI chip. If battery levels are critically low and no external energy is harvested, the audio sampling rate is mathematically throttled from$16\text{kHz}$ to$8\text{kHz}$ :$$F_s(t) = f\big(P_{\text{harv}}(t), , State_{\text{battery}}\big)$$ The processor's Clock Speed is dropped to draw microampere ($\mu\text{A}$ ) levels, extending the lifespan of battery-powered devices for years without internet reliance.
- Variation A (Acoustic Triggered Deep Sleep): The processor remains in Deep Sleep mode perpetually. Only an ultra-low power hardware analog comparator circuit (VAD - Voice Activity Detection) remains active on-chip. The moment a specific decibel threshold is breached, a Wake-up Interrupt is dispatched to the main processor core.
- Variation B (Solar/Ambient Light Optimization): Based on the current harvested from miniature solar panels capturing ambient light on the kitchen counter, the FreeCuli state machine asynchronously dims the brightness matrix of local status LEDs or drops the display refresh rate (FPS).
This power management engine utilizes the FreeCuli Power Abstraction Layer (FC-PAL). Operating without touching the hardware architecture (ARM Power Domains, ESP32 RTC Controller, etc.), the driver layer uses universal power state definitions (e.g., FC_POWER_SAVE_LEVEL_1), enforcing energy optimization as a hardware-agnostic standard across all industrial deployments.
The transition to Edge AI is no longer a futuristic concept; it is an immediate regulatory and operational necessity. As global privacy laws tighten and consumers demand absolute data security within their homes, the centralized cloud dependency model is rapidly becoming obsolete.
The FreeCuli HFSCA standard offers an exit strategy: a privacy-first, zero-cost, offline-native architecture.
We invite global appliance manufacturers, semiconductor engineers, and the open-source community to adopt the FreeCuli standard, eliminate their cloud liability, and build the next generation of truly independent smart kitchen appliances.
ยฉ 2026 FreeCuli. All rights reserved. This document is licensed under CC BY-SA 4.0. The FreeCuli Edge Smart Kitchen Standard is engineered to comply with GDPR Article 25 (Data protection by design and by default) and KVKK Article 4.