Published posts

1. DNS Resolution Explained: Build a TTL Cache and Packet Parser in Python A runnable DNS guide covering resolution paths, response headers, TTL cache latency, and deterministic Python/C experiments.
2. CIDR, Longest Prefix Match, and MTU: Calculate IP Routing Step by Step Calculate CIDR ranges, longest-prefix route choice, and MTU/MSS payload segmentation with runnable Python and C examples.
3. TCP Reliability and Congestion Window: A Runnable Sequence Number Experiment Track TCP sequence numbers, cumulative ACKs, loss, retransmission, and congestion-window changes with safe local experiments.
4. HTTPS and TLS 1.3 Handshake: Keys, Certificates, and RTT in Practice Understand TLS 1.3 message flights, certificate authentication, ephemeral key agreement, and handshake latency with a safe teaching model.
5. HTTP/2, HTTP/3, and CDN Caching: Read Page Speed from a Waterfall A deterministic browser-waterfall model for HTTP/2, HTTP/3, QUIC streams, and CDN cache hits or misses.
6. Forward Proxy vs Reverse Proxy: Connection Paths, Trust Boundaries, and Latency A reproducible guide to forward proxies, reverse proxies, tunnels, TLS boundaries, and latency segments.
7. HTTP CONNECT and HTTPS Proxy Tunnels: TLS Boundaries and Handshake Latency An RFC-based explanation of CONNECT tunnels, encrypted HTTPS payloads, and modeled first-request latency.
8. SOCKS5 Proxy Explained: Protocol Bytes, DNS Resolution Boundaries, and Leakage Risk Decode safe SOCKS5 CONNECT bytes and compare local-DNS and proxy-side hostname resolution boundaries.
9. Reverse Proxy Load Balancing: Queues, Health Checks, and a Reproducible Scheduler Compare round robin and load-aware queue selection while reasoning about health checks and retry boundaries.
10. Proxy Cache Revalidation: Cache-Control, ETag, and Observable Correctness Use an RFC 9111 shared-cache model to calculate MISS, HIT, and 304 revalidation latency and correctness boundaries.

Published resources

1. Network Fundamentals Lab README Setup, no-privilege safety boundary, ten Python experiments, and three C examples.
2. Network fundamentals full lab bundle Bundles Python/C source, fixed scenarios, ten result CSVs, and protocol/proxy figures.
3. DNS TTL results CSV HIT/MISS state, expiry, and latency for four fixed lookups.
4. CIDR and MTU results CSV Longest-prefix route and 3600-byte payload segmentation results.
5. TCP cwnd events CSV Per-round ACK, window, and deterministic retransmission events.
6. TLS 1.3 flight results CSV Message direction, timing, and teaching shared value in a fixed RTT model.
7. HTTP/CDN waterfall results CSV Phase timing for HTTP/2 and HTTP/3 in cold and warm cache models.
8. Proxy path latency results CSV Phase timing for direct access, forward-proxy tunneling, and reverse-proxy cache paths.
9. CONNECT/TLS timeline CSV Records CONNECT authority, tunnel establishment, and the encrypted HTTPS-request boundary.
10. SOCKS5 DNS boundary CSV Stores ATYP, destination bytes, request length, and modeled local DNS counts.
11. Proxy load-balancing queue CSV Compares backend selection and queue waiting for round robin and least queue.
12. Proxy cache revalidation CSV Records MISS, HIT, 304 revalidation, object age, and response latency.
13. Network request path visualizer Adjust TTL, prefixes, loss, handshake RTT, and cache paths in the browser.
14. Network fundamentals topic share card A 1200x630 SVG card for the DNS, TLS, HTTP/3, proxy tunnel, and caching topic hub.

Next notes

1. Add IPv6 and QUIC observation notes
2. Review caching and protocol benefits with real-user metrics

Published posts

1. Reading PyBaMM Fast: Architecture for Battery Modeling and AI Data A PhD-level guide to PyBaMM expression trees, Simulation, model options, metadata, and AI dataset design.
2. PyBaMM EIS Data Generation: Impedance Features and AI Labels Use PyBaMM core EISSimulation to generate impedance spectra, extract features, and align them with aging labels.
3. Generate Battery Aging and EIS AI Datasets with PyBaMM Build a reproducible PyBaMM data factory for SOH, RUL, LLI, LAM, plating, and impedance-feature labels.
4. Training a Battery AI Model with PyBaMM: Predicting SOH and RUL Train scikit-learn regressors on PyBaMM-style EIS features and operating metadata to predict battery SOH and RUL.

Published resources

1. PyBaMM AI Data Lab README Setup, quick run, backend behavior, and output schemas for the PyBaMM battery AI data pipeline.
2. PyBaMM AI Data Lab full bundle Bundles design generation, aging sweeps, EIS sweeps, label building, validation checks, sample CSVs, and figures.
3. PyBaMM sample manifest Stores sample id, model family, parameter set, protocol, temperature, SOC, cycle, split group, and label source.
4. PyBaMM EIS sample spectra CSV Frequency-level impedance output with frequency, Z_re, Z_im, magnitude, phase, backend, and solver status.
5. Battery aging and EIS labels CSV Stores SOH, RUL proxy, LLI, LAM, plating, local resistance, and EIS features.
6. PyBaMM AI data quality report Records duplicate samples, duplicate spectrum points, missing labels, split leakage, and backend usage.
7. PyBaMM to AI data pipeline figure Shows design grid, aging solve, EIS solve, label build, quality gate, and AI split.
8. EIS feature and label schema figure Connects frequency points, impedance features, operating metadata, and SOH/RUL/degradation labels.
9. Aging label sample figure Sample figure showing cycle snapshots, SOH, and local ECM resistance labels.
10. SOH/RUL training metrics CSV Stores group split, MAE, RMSE, R2, label source, and backend used for auditing model results.
11. SOH/RUL held-out predictions CSV Stores held-out true values, predictions, and absolute errors.
12. SOH/RUL feature importance CSV Records random-forest feature importance values for each target model.
13. SOH/RUL training results figure Shows held-out SOH/RUL prediction scatter plots and SOH feature importance.
14. Battery Modeling for AI share card OG share card for the PyBaMM battery modeling, EIS, aging simulation, and AI data hub.

Next notes

1. Add experimental calibration and identifiability notes
2. Add revalidated PyBOP/SEIS comparison notes

Published posts

1. AI Basics Learning Roadmap Separate AI, machine learning, and deep learning before going into implementation details.
2. Machine Learning Workflow Follow the practical path from data and features to training, prediction, and evaluation.
3. Model Training and Evaluation Understand loss, overfitting, train/test splits, accuracy, recall, and F1.
4. Neural Network Basics Move from perceptrons to activation, forward propagation, backpropagation, and training loops.
5. Matrix Calculus for Neural Networks Derive dL/dW for y = Wx + b and verify it with finite differences.
6. Backpropagation as a Computation Graph Trace local gradients through ReLU and softmax cross-entropy in a two-layer MLP.
7. Gradient Descent and Optimizer Geometry Compare gradient descent, momentum, and Adam on a visible quadratic loss surface.
8. Convolution and Receptive Field Math Compute convolution output size, receptive fields, channel mixing, and im2col layout.
9. Transformer Attention Math Hand-calculate Q/K/V scores, softmax weights, masks, multi-head structure, and KV cache.
10. Python AI Mini Practice Run a small scikit-learn classification task and read the experiment output.
11. Handwritten Digit Dataset Basics Read train.csv, test.csv, labels, and the flattened 28 by 28 pixel layout before training the classifier.
12. Handwritten Digit Softmax in C Follow the C implementation from logits and softmax probabilities to confusion matrices and submission export.
13. Handwritten Digit Playground Notes See how the offline classifier was adapted into a browser demo with drawing input and probability output.
14. CIFAR-10 Tiny CNN Tutorial in C Build and train a small convolutional neural network for CIFAR-10 image classification, then read its loss and accuracy output.
15. High-Entropy Traffic Defense Notes Study encrypted metadata leaks, entropy, traffic classifiers, and a defensive Python chaffing prototype.
16. AI Security Threat Modeling Build a defense map with NIST adversarial ML, MITRE ATLAS, and OWASP LLM risks.
17. Adversarial Examples and Robust Evaluation Evaluate clean and perturbed accuracy with an FGSM-style digits experiment.
18. Data Poisoning and Backdoor Defense Study poison rate, trigger behavior, attack success rate, and training pipeline controls.
19. Model Privacy and Extraction Defense Measure membership inference signal and surrogate fidelity against a local toy model.
20. LLM, RAG, and Agent Security Separate instructions from data and enforce tool permissions against indirect prompt injection.

Published resources

1. Python AI practice code guide The article includes a runnable scikit-learn classification script.
2. digit_softmax_classifier.c The C source for the handwritten digit softmax classifier.
3. train.csv.zip Compressed handwritten digit training set with 42000 labeled samples.
4. test.csv.zip Compressed handwritten digit test set with 28000 unlabeled samples.
5. sample_submission.csv The official submission format example for checking the final output columns.
6. submission.csv The prediction file generated by the current C project.
7. digit-playground-model.json The compact softmax demo model and sample set used by the browser playground.
8. digit-sample-grid.svg A small handwritten digit preview grid extracted from the training set.
9. Handwritten digit project bundle Contains the source file, compressed datasets, submission files, browser model, and preview grid.
10. cifar10_tiny_cnn.c source Single-file C tiny CNN with CIFAR-10 loading, convolution, pooling, softmax, and backpropagation.
11. model_weights.bin sample weights Model weights generated by one local small-sample run.
12. test_predictions.csv sample predictions Sample test prediction output from the CIFAR-10 tiny CNN.
13. CNN project explanation PDF Companion explanation material for the CNN project.
14. Virtual Mirror redacted code skeleton A redacted mld_chaffing_v2.py control-flow skeleton with secrets, node topology, and target lists removed.
15. Virtual Mirror stress-test template A redacted CSV template for CPU, memory, peak threads, pulse rate, latency, and error measurements.
16. Virtual Mirror classifier-evaluation template A CSV template for TP, FN, FP, TN, accuracy, precision, recall, F1, ROC-AUC, entropy, and JS divergence.
17. Virtual Mirror resource notes Notes explaining why the public resources include only redacted code, test templates, and architecture context.
18. AI Security Lab README Setup, safety boundaries, and quick-run commands for the AI Security series.
19. AI Security Lab full bundle Includes safe toy scripts, result CSVs, risk register, attack-defense matrix, and architecture diagram.
20. AI security risk register CSV risk register template for AI threat modeling and release review.
21. AI attack-defense matrix Maps attack surface, toy demo, metric, and defensive control into one CSV table.
22. AI Security Lab architecture diagram Shows threat modeling, robustness, data integrity, model privacy, and RAG guardrails.
23. FGSM digits robustness script FGSM-style perturbation and accuracy-drop experiment for a local digits classifier.
24. Data poisoning and backdoor toy script Demonstrates poison rate, trigger behavior, and attack success rate on digits.
25. Model privacy and extraction toy script Outputs membership AUC, target accuracy, surrogate fidelity, and surrogate accuracy.
26. RAG prompt injection guard toy script Uses a deterministic toy agent to demonstrate external-data demotion and tool-policy blocking.
27. Deep Learning Math Lab README Setup commands, script entry points, generated outputs, and figure notes for the math series.
28. Deep learning math full lab bundle Bundles NumPy scripts, CSV outputs, formula diagrams, loss contours, convolution figures, and attention heatmaps.
29. Gradient check results CSV Stores MSE analytic gradients, finite-difference gradients, and error norms.
30. Optimizer path CSV Step-by-step coordinates and loss for gradient descent, momentum, and Adam on a 2D quadratic.
31. Attention weights CSV Scores, softmax weights, and context vectors for a three-token scaled dot-product attention example.
32. Deep learning math figure set Includes matrix shapes, computation graphs, loss contours, convolution scans, and attention heatmaps.
33. Deep learning math interactive visualizer Browser modules for gradient checking, optimizer paths, convolution output size, and attention heatmaps.
34. Deep Learning topic share card A 1200x630 SVG card for sharing the Deep Learning / CNN topic hub.
35. Machine Learning From Scratch share card A 1200x630 SVG card for the K-means, Iris, and ML workflow topic hub.
36. Student AI Projects share card A 1200x630 SVG card for handwritten digits, C classifiers, and browser demos.
37. CNN convolution scan animation An 8-second Remotion animation showing how a 3x3 convolution kernel scans an input and builds a feature map.

Current route

1. AI Basics Learning Roadmap Learning path step
2. Machine Learning Workflow Learning path step
3. Model Training and Evaluation Learning path step
4. Neural Network Basics Learning path step
5. Matrix Calculus for Neural Networks Learning path step
6. Backpropagation as a Computation Graph Learning path step
7. Gradient Descent and Optimizer Geometry Learning path step
8. Convolution and Receptive Field Math Learning path step
9. Transformer Attention Math Learning path step
10. LLM Visualizer Learning path step
11. Python AI Mini Practice Learning path step
12. Handwritten Digit Dataset Basics Learning path step
13. Handwritten Digit Softmax in C Learning path step
14. Handwritten Digit Playground Notes Learning path step
15. CIFAR-10 Tiny CNN Tutorial in C Learning path step
16. High-Entropy Traffic Defense Notes Learning path step
17. AI Security Threat Modeling Learning path step
18. Adversarial Examples and Robust Evaluation Learning path step
19. Data Poisoning and Backdoor Defense Learning path step
20. Model Privacy and Extraction Defense Learning path step
21. LLM, RAG, and Agent Security Learning path step

Next notes

1. Add more image-classification and error-analysis cases
2. Turn common metrics into a quick reference
3. Add more AI security defense experiment notes

Published posts

1. Character Image to SVG Animation Asset Pipeline: ComfyUI, Grounded-SAM, OpenPose, and vtracer The first article in the 2D animation principles column: use ComfyUI IP-Adapter, OpenPose exploded layout, Grounded-SAM matting, and vtracer to build rig assets.

Published resources

1. Iris.csv dataset The 150-sample Iris dataset used by the K-means article.
2. Iris_sort_K_mean.c source Includes standardization, K-means++ initialization, restarts, and SSE selection.
3. K-means flowchart SVG flowchart for the C program execution path.
4. Cluster visualization A 2D scatter projection using petal length and petal width.
5. K-means zip package Contains dataset, source code, flowchart, and visualization.
6. Gaoshu Lianxi PDF A public advanced calculus practice PDF for review or printing.
7. Basic SAM + vtracer script Local reference script for character segmentation, PNG layers, and SVG vectorization.
8. ComfyUI + Grounded-SAM production script Coordinates local ComfyUI, Grounding DINO, SAM, and vtracer to build SVG rig assets with stable IDs.
9. ComfyUI exploded character workflow template Editable API workflow template for IP-Adapter identity locking and ControlNet OpenPose exploded layout.
10. Exploded OpenPose skeleton Layout reference for separated head, torso, arms, and legs; export to PNG for ControlNet if needed.
11. Animation part prompt list Grounding DINO prompts for semantic matting: head, left arm, torso, tail, accessory, and more.
12. Algorithm Visualization share card A 1200x630 SVG card for eight queens, backtracking, bitmasks, and the playground.
13. K-means iteration animation A Remotion clip showing sample assignment, centroid updates, and SSE reduction.
14. Eight queens backtracking animation A Remotion clip showing row-by-row search, conflict pruning, and backtracking.

Current route

1. Eight queens with classic backtracking Learning path step
2. Bitwise optimization for the eight queens problem Learning path step
3. K-means clustering on the Iris dataset in C Learning path step
4. K-means companion downloads Learning path step

Next notes

1. Add more runnable algorithm examples
2. Expand downloadable example inputs

Published posts

1. Welcome to haotianblog: what this bilingual technical site covers An introduction to the bilingual site, its technical focus, and the kinds of articles and resources it will publish.

Published resources

1. SEO distribution short-video storyboards Four 45-60 second storyboard scripts ready for later Remotion production.

Next notes

1. Keep documenting deployment and maintenance notes
2. Clarify the content sync workflow