BBR (Bottleneck Bandwidth and Round-trip propagation time), developed by Neal Cardwell, Yuchung Cheng, and others at Google, is one of the most advanced model-based congestion control algorithms available today. Unlike traditional loss-based algorithms (Reno, CUBIC), BBR explicitly models the network path by directly measuring bottleneck bandwidth and propagation delay, sending data at the BDP (Bandwidth-Delay Product) rate at the bottleneck point. BBR’s core insight is that packet loss does not equal congestion. On deep-buffered (Bufferbloat) or wireless links, packet loss can be caused by channel noise or excessive buffer queuing rather than genuine link saturation. BBR actively measures bandwidth and latency to precisely control the sending rate, rather than passively waiting for loss signals.

What Is a Digital Image Computer vision operates on digital images. At its core, a digital image is just a 2D array of pixels. Pixels and Grayscale Images A grayscale image is the simplest form—each pixel stores a single brightness value from 0 (pure black) to 255 (pure white). In Python, a grayscale image of width W and height H is a 2D array with shape (H, W): python 1 2 3 4 import numpy as np # Create a 100x100 gray square (brightness 128) gray_img = np.full((100, 100), 128, dtype=np.uint8) uint8 ranges from 0 to 255 because 8 bits can represent 2^8 = 256 unique values. If a pixel value exceeds its range, wrap around occurs: 255 + 1 becomes 0, not 256 (like an odometer rolling over). Use cv2.add() (saturating arithmetic) or np.clip() to prevent wrap around.

Model Loading and Inference Across Versions Ultralytics Unified API (Works with v8/11/26) python 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 from ultralytics import YOLO # ========== YOLOv8 ========== model_v8 = YOLO("yolov8n.pt") # nano model_v8 = YOLO("yolov8s.pt") # small model_v8 = YOLO("yolov8m.pt") # medium model_v8 = YOLO("yolov8l.pt") # large model_v8 = YOLO("yolov8x.pt") # extra large # ========== YOLO11 ========== model_11 = YOLO("yolo11n.pt") # nano model_11 = YOLO("yolo11s.pt") # small model_11 = YOLO("yolo11m.pt") # medium model_11 = YOLO("yolo11l.pt") # large model_11 = YOLO("yolo11x.pt") # extra large # ========== YOLO26 (2026 latest) ========== model_26 = YOLO("yolo26n.pt") # nano recommended for edge deployment model_26 = YOLO("yolo26s.pt") # small model_26 = YOLO("yolo26m.pt") # medium model_26 = YOLO("yolo26l.pt") # large model_26 = YOLO("yolo26x.pt") # extra large Image Detection Hands-on Example python 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 from ultralytics import YOLO # Load model (YOLO26 example) model = YOLO("yolo26n.pt") # Single image detection results = model("test.jpg", conf=0.25, iou=0.45) # Process results for result in results: boxes = result.boxes # Detection boxes masks = result.masks # Segmentation masks probs = result.probs # Classification probabilities # Print detection results for box in boxes: print(f"Class: {result.names[int(box.cls)]}, " f"Confidence: {box.conf.item():.3f}, " f"Coordinates: {box.xyxy.tolist()[0]}") # Save visualization results result.save("result.jpg") Video Detection Hands-on Example python 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 from ultralytics import YOLO model = YOLO("yolo26n.pt") # Video file detection results = model.predict( source="input.mp4", save=True, # Save result video conf=0.3, show=False, # Whether to display in real-time stream=True # Stream processing to save memory ) # Process frame by frame for result in results: # Custom post-processing logic pass Real-time Camera Detection python 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 from ultralytics import YOLO import cv2 model = YOLO("yolo26n.pt") # Open camera cap = cv2.VideoCapture(0) # 0 is default camera while cap.isOpened(): ret, frame = cap.read() if not ret: break # Inference results = model(frame, verbose=False) # Draw results annotated_frame = results[0].plot() # Display cv2.imshow("YOLO Real-time", annotated_frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() Version-specific Code Differences Feature YOLOv8 YOLO11 YOLO26 YOLOv9 YOLOv10 Unified API ✅ ✅ ✅ ❌ Separate repo ❌ Separate repo No NMS ❌ ❌ ✅ ❌ ✅ DFL Module ✅ ✅ ❌ Removed ✅ ✅ MuSGD Optimizer ❌ ❌ ✅ ❌ ❌ Export Compatibility Good Good Best Fair Fair Results Object API Deep Dive The model() or model.predict() call returns a list of Results objects. Each Results object encapsulates all inference outputs for a single image. Understanding its internal structure is essential for downstream processing.

Why I built a surveillance camera with ESP32-S3 before, and it worked well. Later, while rummaging through a drawer, I found an AI-Thinker ESP32-CAM development board — that classic board costing about ten bucks with a built-in OV2640 camera and TF card slot. No reason to let it go to waste, so I built another one: ai-thinker-esp32-cam. This time I wrote the firmware from scratch using ESP-IDF again, with similar capabilities to the previous project but with lots of adaptations for the AI-Thinker board. Here’s what it ended up doing:

The LMS Algorithm The core problem in adaptive filtering is: given a reference signal x(n) and a desired signal d(n), find a filter coefficient vector w such that the output y(n) approximates d(n). The Least Mean Square (LMS) algorithm is the most classic solution to this problem, proposed by Widrow and Hoff in 1960. The core idea is to take one step down the steepest gradient of the error surface at each iteration — stochastic gradient descent.

The previous article covered the initial configuration setup. This one documents the adjustments after two weeks of running: expanding from single vendor to a four-tier model pool, adding fallback chains, hitting the GLM-4.5-air trap of analyzing without writing code. This post covers: fallback strategy design, complete free model pool inventory and analysis, concurrency control configuration, and the decision process for GLM-4.5-air replacement. After the previous article’s initial configuration, I ran it for two weeks — all the issues that needed fixing surfaced.

This article is based on Rust 1.80+. If you have a Go or Java background, the ownership system might be the most confusing concept in Rust. Go has a garbage collector (GC), and you rarely need to worry about when memory is freed. Rust takes a completely different approach—compile-time ownership rules guarantee memory safety, with zero runtime overhead. The ownership system is the soul of Rust. It solves two classic problems: how to guarantee memory safety without using GC, and how to avoid data races. In other languages, these problems either require runtime checks or require extreme developer caution.

I have several cameras at home — a few Xiaomi cameras, some DIY ESP32 cameras, and multiple Raspberry Pi CSI cameras. I’d been using cloud storage solutions, but I was never comfortable with them: vendor lock-in, network dependency, and the costs add up. So I decided to build my own NVR system, called MiBeeNvr. Why Build MiBeeNvr To be honest, I was never satisfied with existing cloud storage solutions. Take Xiaomi cameras, for example. By default, you can only view them through the Mi Home app. Recordings are either stored on an SD card (limited capacity, frequent plugging/unplugging) or in the cloud. Cloud storage costs tens of dollars per month, and there’s the privacy concern — you never know when the manufacturer might use your video data for AI training or sell it to third parties. Not to mention vendor lock-in — switching platforms is nearly impossible.

Learning Path and Version Selection Guide Version Selection Guide Version Release Date Development Team Use Cases Recommendation Index YOLO26 2026.01 Ultralytics Official Edge deployment, CPU inference, industrial applications ⭐⭐⭐⭐⭐ YOLOv8 2023.01 Ultralytics Official Beginner learning, complete ecosystem, general scenarios ⭐⭐⭐⭐⭐ YOLO11 2024.09 Ultralytics Official Efficiency optimization, lightweight deployment ⭐⭐⭐⭐ YOLOv10 2024.05 Tsinghua University Research exploration, NMS-free end-to-end ⭐⭐⭐⭐ YOLOv9 2024.01 National Taiwan University High precision, small object detection ⭐⭐⭐⭐ YOLOv12 2025.02 Buffalo University + Chinese Academy of Sciences Attention mechanism research ⭐⭐⭐ Learning Path Recommendations Beginner Stage (1-2 weeks): Start with YOLOv8, master basic concepts and API usage Intermediate Stage (2-3 weeks): Learn custom dataset training, parameter tuning and optimization Advanced Stage (2-3 weeks): Learn model deployment, engineering implementation Research Stage (ongoing): Explore new features in YOLO11, YOLO26, YOLOv9/v10/v12 Complete YOLO Development History Timeline Version Release Date Core Innovation Milestone Significance YOLOv1 2015.06 Pioneer single-stage detection Foundation for real-time detection YOLOv2 2016.12 Batch Normalization, Anchor Dual improvement in accuracy and speed YOLOv3 2018.04 Multi-scale detection, residual networks Industry standard YOLOv4 2020.04 CSPDarknet, Mosaic Peak of engineering implementation YOLOv5 2020.06 PyTorch framework, user-friendly Highest adoption rate YOLOv7 2022.07 E-ELAN, reparameterization Balance between speed and accuracy YOLOv8 2023.01 C2f, Anchor-Free, unified framework Ultralytics unified ecosystem YOLOv9 2024.01 GELAN, PGI programmable gradient Training efficiency revolution YOLOv10 2024.05 NMS-free, efficiency-precision tradeoff End-to-end detection YOLO11 2024.09 Architecture optimization, parameter reduction Efficiency optimized version YOLOv12 2025.02 Area Attention mechanism Attention architecture YOLO26 2026.01 DFL-free, NMS-free, 43% CPU optimization Edge computing new standard Core Principles and Version Comparison Ultralytics Official Main Line Versions YOLOv8 Core Features:

Destructive Interference Active noise cancellation (ANC) builds on a simple physical principle: destructive interference. Two sound waves of the same frequency and opposite phase cancel each other out. The original noise signal: $$ p_n(t) = A \cos(2\pi ft + \phi) $$The anti-noise generated by the ANC system: $$ p_c(t) = -A \cos(2\pi ft + \phi) = A \cos(2\pi ft + \phi + \pi) $$Superposition: $$ p_{\text{total}} = p_n(t) + p_c(t) = 0 $$That is the ideal case. In a real ANC headset, the anti-noise travels from the speaker to the eardrum through an acoustic path, and the electronics introduce latency. If the phase shift is off by even a few degrees, or the amplitudes don’t match, cancellation degrades fast.